Community Detection

Many complex networks display a mesoscopic structure with groups of nodes sharing many links with the other nodes in their group and comparatively few with nodes of different groups. This feature is known as community structure and encodes precious information about the organization and the function of the nodes. Many algorithms have been proposed but it is not yet clear how they should be tested. Recently we have proposed a general class of undirected and unweighted benchmark graphs, with heterogenous distributions of node degree and community size. An increasing attention has been recently devoted to develop algorithms able to consider the direction and the weight of the links, which require suitable benchmark graphs for testing. In this paper we extend the basic ideas behind our previous benchmark to generate directed and weighted networks with built-in community structure. We also consider the possibility that nodes belong to more communities, a feature occurring in real systems, like social networks. As a practical application, we show how modularity optimization performs on our new benchmark.

Abstract: Community detection is an important research topic in complex networks. We present the employment of a genetic algorithm to detect communities in complex networks which is based on optimizing network modularity. It does not need any prior knowledge about the number of communities. Its performance is tested on two real life networks with known community structures and a set of synthetic networks. As the performance measure an information theoretical metric variation of information is used. The results are promising ...

We propose a simple method to extract the community structure of large networks. Our method is a heuristic method that is based on modularity optimization. It is shown to outperform all other known community detection method in terms of computation time. Moreover, the quality of the communities detected is very good, as measured by the so-called modularity. This is shown first by identifying language communities in a Belgian mobile phone network of 2.6 million customers and by analyzing a web graph of 118 million nodes and more than one billion links. The accuracy of our algorithm is also verified on ad-hoc modular networks.

Community detection in a social network is a well-known problem that has been studied in computer science since early 2000. The algorithms available in the literature mainly follow two strategies, one, which allows a node to be a part of multiple communities with equal membership, and the second considers a disjoint partition of the whole network where a node belongs to only one community. In this paper, we proposed a novel community detection algorithm which identifies fuzzy-rough communities where a node can be a part of many groups with different memberships of their association. The algorithm runs on a new framework of social network representation based on fuzzy granular theory. A new index viz. normalized fuzzy mutual information, to quantify the goodness of detected communities is used. Experimental results on benchmark data show the superiority of the proposed algorithm compared to other well known methods, particularly when the network contains overlapping communities.

Online communities are one of the powerful digital sources for businesses to analyze online users’ behavioral data. In this sense, it is important for practitioners to know how to motivate community members, to keep them amused and regularly engaged in the community. But, practitioners should be aware that different user types exist in online communities, and they should understand these members’ diverse needs to manage these communities successfully and to give a better service to their members. Concordantly, this study focuses on the problem of the existence of different user types in online communities and development of different strategies for their motivation, involvement, and communication. Unlike previous studies, this study considers theories across managerial and social domains, conducts social network analysis, and considers users’ contribution patterns in an online community to identify particular user types in an online community. The study also presents motivational strategies for practitioners to keep each type of users frequently amused in online communities.

Credit card fraud occurs when user provides their information to the unknown persons or stolen by the unknown persons, that information can be used for unauthorized online purchase and some other situation. Data mining techniques are employed to study the patterns and characteristics of normal and abnormal transactions based on normalized and anomalies data. In order to detect fraud activities and feature patterns associated with financial activities, we are proposing novel fraud detection framework, Codetect. Wide variety of applications is associated with anomaly detection such as fraud detection and network intrusion detection which referees to pattern finding problems in data.

To understand the overlapping community structure in complex networks, a great deal of methods have been proposed by researchers from different areas. Community formation game is a game-theoretical view for this problem. In this article, we develop an new concept named social distance to describe a natural phenomenon in real world reflecting the strength of connections between different individuals. Some improvements have been given to acquire better performance of detecting the overlapping community structure. We illustrate them by conducting a series of experiments both on real-world and benchmark networks.

Community detection is an important research topic in complex networks. We present the employment of a genetic algorithm to detect communities in complex networks which is based on optimizing network modularity. It does not need any prior knowledge about the number of communities. Its performance is tested on two real life networks with known community structures and a set of synthetic networks. As the performance measure an information theoretical metric variation of information is used. The results are promising and in some cases better than previously reported studies.

We survey some of the concepts, methods, and applications of community detection, which has become an increasingly important area of network science. To help ease newcomers into the field, we provide a guide to available methodology and open problems, and discuss why scientists from diverse backgrounds are interested in these problems. As a running theme, we emphasize the connections of

Community structure is one of the most important features of real networks and reveals the internal organization of the nodes. Many algorithms have been proposed but the crucial issue of testing, i.e. the question of how good an algorithm is, with respect to others, is still open. Standard tests include the analysis of simple artificial graphs with a built-in community structure, that the algorithm has to recover. However, the special graphs adopted in actual tests have a structure that does not reflect the real properties of nodes and communities found in real networks. Here we introduce a new class of benchmark graphs, that account for the heterogeneity in the distributions of node degrees and of community sizes. We use this new benchmark to test two popular methods of community detection, modularity optimization and Potts model clustering. The results show that the new benchmark poses a much more severe test to algorithms than standard benchmarks, revealing limits that may not be apparent at a first analysis.

Social network analysis has become a major subject in recent times, bringing also several challenges in the computer science field. One aspect of the social network analysis is the community detection problem, which is seen as a graph clustering problem. However, social networks are more than a graph, they have an interesting amount of information derived from its social aspect, such as profile information, content sharing and annotations, among others. Most of the community detection algorithms use only the structure of the network, i.e., the graph. In this paper we propose a new method which uses the semantic information along with the network structure in the community detection process. Thus, our method combines an algorithm for optimizing modularity and an entropy–based data clustering algorithm, which tries to find a partition with low entropy and keeping in mind the modularity.

Community detection has arisen as one of the most relevant topics in the field of graph mining, principally for its applications in domains such as social or biological networks analysis. Different community detection algorithms have been proposed during the last decade, approaching the problem from different perspectives. However, existing algorithms are, in general, based on complex and expensive computations, making them unsuitable for large graphs with millions of vertices and edges such as those usually found in the real world.

The advent of business oriented and social networking sites on the Internet have seen a huge increase in number of people using them in recent years. With the expansion of Web 2.0, new types of websites have emerged such as online social networks, blogs and wikis. Their popularity has resulted in exponential growth of information on the web and interactions overload thus making it harder to access useful or relevant information. Recommender systems are one of the applications employed to address this problem by filtering relevant information and enhancing user experience. They traditionally use either the content of items of the websites (content-filtering recommender systems) or the collaboration between the users and items such as rating (collaborative-filtering recommender systems) or a combination of them (hybrid recommender systems). However due to the nature of data they use, they all have one or more weaknesses such as cold start, sparsity of data, scalability problems and overspecialised recommendation. Social networks and other similar websites have new types of data which can be used in recommender systems thus have the potential to overcome these shortcomings. However without a good understanding of the properties and structure of these online social websites, the applications can not be accurate. This paper presents an empirical measurement study of the properties and structure of one such social websites. It examines an online movie database, and the interactions between reviewers and attempts to construct a social network graph based on the network of reviewers. The resulting network is confirmed as the power-law, small-world and scalefree. It identifies the highly connected clusters and shows that the content of these subgroups are diversified and not limited to similar tags. Finally the implication of these finding is discussed in order to enhance current recommender systems enabling them to provide diverse results while overcome their shortcomings.

In this paper, we study musical knowledge extraction and discrimination. Specifically, we propose a method for automatic extraction of drums rhythmic patterns of music and the rhythmic summarization of a set of songs from the same artist. A musical piece is generally formed of one or more predefined rhythmic patterns and such patterns are composed of Rhythmic Cells (RC), which are groups of rhythmic figures derived from n-th division of a larger rhythmic figure. At the pre-processing and encoding phase, the RCs of drums percussion lines are represented in Duration-Weighted Notation (DWN). Then, the vector of DWM is encoded to be free of the dimensional dependence on the number of figures in the RC. After that, a network is constructed from the encoded DWM using the method proposed in this paper. We find that the rhythmic patterns of the musical work are related to the formation of communities in the network. In this work, two community detection algorithms are used: Louvain algorithm for the disjoint community detection and Bayesian Nonnegative Matrix Factorization (BNMF) algorithm  for detecting overlapping communities. Moreover, a new measure for quantifying the relevance of communities to differentiate types of rhythmic patterns is introduced. The proposed technique has been applied to automatic extraction of drums rhythmic pattern of the song "Drive my car'' by The Beatles. Experimental results show good performance of the proposed method.

In complex network research clique percolation, introduced by Palla et al., is a deterministic community detection method, which allows for overlapping communities and is purely based on local topological properties of a network. Here we present a sequential clique percolation algorithm (SCP) to do fast community detection in weighted and unweighted networks, for cliques of a chosen size. This method is based on sequentially inserting the constituent links to the network and simultaneously keeping track of the emerging community structure. Unlike existing algorithms, the SCP method allows for detecting k-clique communities at multiple weight thresholds in a single run, and can simultaneously produce a dendrogram representation of hierarchical community structure. In sparse weighted networks, the SCP algorithm can also be used for implementing the weighted clique percolation method recently introduced by Farkas et al. The computational time of the SCP algorithm scales linearly with the number of k-cliques in the network. As an example, the method is applied to a product association network, revealing its nested community structure.

The problem of defining P2P overlays where peers characterized by similar interests are directly connected is currently an important research issue. We have recently proposed a two layer P2P architecture where the first layer exploits a gossip algorithm for the detection of communities of peers characterized by similar interests, while the second layer defines a DHT storing the profiles of the communities detected in the first layer. The DHT is exploited by peers joining the system to find out a community matching its interests. This paper investigates a DHT based approach supporting a similarity based search of user profiles. Our approach exploits Locality Sensitive Hashing to support the similarity indexing. The paper investigates several types of profiles to model user interests and evaluates the indexing mechanisms of the DHT with respect to the different types. Experimental evaluation has been conducted by considering a real data set.

The proposed survey discusses the topic of community detection in the context of Social Media. Community detection constitutes a significant tool for the analysis of complex networks by enabling the study of mesoscopic structures that are often associated with ...

In der Nacht vom 19. März 2003 begann der Angriff der USA auf den Irak. Der damalige Verteidigungsminister Donald Rumsfeld hatte die Doktrin "shock and awe-ausgegeben: die militärische Überlegenheit der USA sollte den irakischen Truppen und der irakischen Führung durch eine schnelle Erlangung der Lufthoheit und einem blitzkrieghaften Nachrücken der Bodentruppen demonstriert werden. Dem militärischen Diskurs der Zeit 1 entsprechend sollte bei vernetzter Operationsführung in einer Kombination aus bisher nicht gekannter Informationsüberlegenheit, daraus resultierender situativer Agilität bei gleichzeitig überwältigender Feuerkraft der Feind schlagartig bezwungen werden. Tatsächlich war die Lufthoheit über dem Irak innerhalb von 6 Stunden erreicht. Die Tarnkappenflugzeuge der USA schrieben sich nicht als Zeichen auf den irakischen Radarschirmen ein, und dessen Luftabwehr konnte nur blind ein Feuerwerk am nächtlichen Himmel verrichten, das zugleich weltweit an den Fernsehschirmen verfolgt wurde. Auch stieg nur ein einziger irakischer Abfangjäger auf. Nach dessen Abschuss bekam das Personal der irakischen Luftwaffenbasen die Nachricht "You Fly -You Die-per Funk und abgeworfenen Flugblättern zu hören und zu lesen. Ab diesem Zeitpunkt was das amerikanische Militär in der Lage, jeden Punkt im Irak mit nahezu beliebiger Feuerkraft anzusteuern. Trotz dieser nie da gewesen Kapazität der Ortung etablierte sich keine Ordnung im Irak. Im folgenden soll im Rahmen einer medienwissenschaftlichen Analyse zeitgenössischer militärischer Diskurse und Medientechniken der Frage nachgegangen werden, wie das amerikanische Militär auf dieses Problem reagiert hat. Dabei wird aufzuzeigen sein, dass in den militärischen Debatten und Projekten das Ortungs-und Ordnungsproblem von Metaphern der Landnahme auf Formate der Ortung und Beherrschung sozialer Ensembles sich verschoben hat. Die neuen Kriege, so die These, brauchen soziale Medien und spielen sich in und mit diesen ab. In diesem Beitrag wird der 1 Stefan Kaufmann, -Network Centric Warfare. Den Krieg Netzwerktechnisch Denken,‖ in Politiken Der Medien, ed. Daniel Gethmann and Markus Stauff (Berlin -Zürich: Diaphanes Verlag, 2005), 245-264.

A fuzzy overlapping community is an important kind of overlapping community in which each node belongs to each community to different extents. It exists in many real networks but how to identify a fuzzy overlapping community is still a challenging task. In this work, the concept of local random walk and a new distance metric are introduced. Based on the new distance measurement, the dissimilarity index between each node of a network is calculated firstly. Then in order to keep the original node distance as much as possible, the network structure is mapped into low-dimensional space by the multidimensional scaling (MDS). Finally, the fuzzy c-means clustering is employed to find fuzzy communities in a network. The experimental results show that the proposed algorithm is effective and efficient to identify the fuzzy overlapping communities in both artificial networks and real- world networks.

A community within a network is a group of vertices densely connected to each other but less connected to the vertices outside. The problem of detecting communities in large networks plays a key role in a wide range of research areas, e.g. Computer Science, Biology and Sociology.

We propose a novel algorithm, FURS (Fast and Unique Representative Subset selection) to deterministically select a set of nodes from a given graph which retains the underlying community structure. FURS greedily selects nodes with high-degree centrality from most or all the communities in the network. The nodes with high-degree centrality for each community are usually located at the center rather than the periphery and can better capture the community structure. The nodes are selected such that they are not isolated but can form disconnected components. The FURS is evaluated by means of quality measures like coverage, clustering coefficients, degree distributions and variation of information. Empirically, we observe that the nodes are selected such that most or all of the communities in the original network are retained. We compare our proposed technique with state-of-the-art methods like SlashBurn, Forest-Fire, Metropolis and Snowball Expansion sampling techniques. We evaluate FURS on several synthetic and real-world networks of varying size to demonstrate the high quality of our subset while preserving the community structure. The subset generated by the FURS method can be effectively utilized by model based approaches with out-of-sample extension properties for inferring community affiliation of the large scale networks. A consequence of FURS is that the Raghvendra Mall selected subset is also a good candidate set for simple diffusion model. We compare the spread of information over time using FURS for several real world networks with random node selection, hubs selection, spokes selection, high eigenvector centrality, high Pagerank, high betweenness centrality and low betweenness centrality based representative subset selection.

The paper puts forward and discusses a 1/2 order net-grid-type analog fractance circuit. Furthermore, it finds out that our 1/2 order net-grid analog fractance circuit is superior to the classical 1/2 order tree-type one by comparing the structure and function of each other. On this bases, it further puts forward and discusses a general recursive net-grid-type analog fractance circuit for any order fractional calculus. The results educing by the paper are the basis for further theoretic research and engineering implement for structuring fractance circuit of any order fractional calculus.

One important problem in target advertising and viral marketing on online social networking sites is the efficient identification of communities with common interests in large social networks. Existing methods involve large scale community detection on the entire social network before determining the interests of individuals within these communities. This approach is both computationally intensive and may result in communities without a common interest. We propose an efficient approach for detecting communities that share common interests on Twitter. Our approach involves first identifying celebrities that are representative of an interest category before detecting communities based on linkages among followers of these celebrities. We also study the characteristics of these communities and the effects of deepening or specialization of interest.

Laplacian mixture models identify overlapping regions of influence in unlabeled graph and network data in a scalable and computationally efficient way, yielding useful low-dimensional representations. By combining Laplacian eigenspace and finite mixture modeling methods, they provide probabilistic or fuzzy dimensionality reductions or domain decompositions for a variety of input data types, including mixture distributions, feature vectors, and graphs or networks. Provable optimal recovery using the algorithm is analytically shown for a nontrivial class of cluster graphs. Heuristic approximations for scalable high-performance implementations are described and empirically tested. Connections to PageRank and community detection in network analysis demonstrate the wide applicability of this approach. The origins of fuzzy spectral methods, beginning with generalized heat or diffusion equations in physics, are reviewed and summarized. Comparisons to other dimensionality reduction and clustering methods for challenging unsupervised machine learning problems are also discussed.

Recommender systems usually propose items to single users. However, in some domains like Mobile IPTV or Satellite Systems it might be impossible to generate a program schedule for each user, because of bandwidth limitations. A few approaches were proposed to generate group recommendations. However, these approaches take into account that groups of users already exist and no recommender system is able to detect intrinsic users communities. This paper describes an algorithm that detects groups of users whose preferences are similar and predicts recommendations for such groups. Groups of different granularities are generated through a modularity-based Community Detection algorithm, making it possible for a content provider to explore the trade off between the level of personalization of the recommendations and the number of channels. Experimental results show that the quality of group recommendations increases linearly with the number of groups created.

Twitter is a platform where users may able to share their thoughts, news, and other information. With Twitter, we can analyze social context with computing system called social computing. Social computing is present as a way to take advantage of the presence of big data. One method that can be done is by using Social Network Analysis (SNA) which can shape patterns of social interactions that occur between individuals in the community. The study, entitled "Influencer Analysis for Alternative Marketing Strategy Of KompasTV Using Social Network Model With Keyword “kompastv” on Twitter aims to detect and determine influencers in every community to market the product (television program).
The research was conducted by collecting all the tweets containing the conversation about KompasTV without limitation the location where the tweet was made to serve the population. For the sample, the data used is the tweet that contains a conversation about KompasTV replies, mentions, and retweets. This research is a descriptive study using SNA. Sizes studied are degree centrality, closeness centrality, betweeness centrality, eigenvector centrality and community detection.
From the results in 1025 nodes or account and 888 edges or interactions on the social conversation about KompasTV. There are eight accounts that become influencers in the network on the seven largest communities.

Laplacian mixture models identify overlapping regions of influence in unlabeled graph and network data in a scalable and computationally efficient way, yielding useful low-dimensional representations. By combining Laplacian eigenspace and finite mixture modeling methods, they provide probabilistic or fuzzy dimensionality reductions or domain decompositions for a variety of input data types, including mixture distributions, feature vectors, and graphs or networks. Provable optimal recovery using the algorithm is analytically shown for a nontrivial class of cluster graphs. Heuristic approximations for scalable high-performance implementations are described and empirically tested. Connections to PageRank and community detection in network analysis demonstrate the wide applicability of this approach. The origins of fuzzy spectral methods, beginning with generalized heat or diffusion equations in physics, are reviewed and summarized. Comparisons to other dimensionality reduction and clus...

A large body of work has been devoted to defining and identifying clusters or communities in social and information networks, i.e., in graphs in which the nodes represent underlying social entities and the edges represent some sort of interaction between pairs of nodes. Most such research begins with the premise that a community or a cluster should be thought of

The Internet-of-things (IoT) networks are witnessing a drastic increase over the years. Twenty billion devices connected to the Internet are expected in 2022. The need for identifying communities within such networks can serve as a strong complexity reduction mean for many discovery and identification services. The idea of communities in IoT networks is also motivated by the emerging concept of socializing IoT devices. In this paper, we investigate the application of two community detection algorithms, namely Louvain and Bron-Kerbosch algorithms, on IoT networks usually represented by large-scale graphs. The objective is to convert the complex IoT network into multiple overlapping and non-overlapping communities where its elements share common characteristics. Starting from a real-world IoT networks, we use its dataset to extract community-structured IoT network based on different types of relationships such as co-location, owner social relationships, and autonomously build object relationships among objects. Our analysis showcases how community detection algorithms structure the IoT network into communities based on the different relationships established between objects.

To understand the overlapping community structure in complex networks, a great deal of methods have been proposed by researchers from different areas. Community formation game is a game-theoretical view for this problem. In this article, we develop a new concept named social distance to describe a natural phenomenon in real world reflecting the strength of connections between different individuals. Some improvements have been given to acquire better performance of detecting the overlapping community structure. We illustrate them by conducting a series of experiments both on real-world and benchmark networks.

We use techniques from network science to study correlations in the foreign exchange (FX) market over the period 1991-2008. We consider an FX market network in which each node represents an exchange rate and each weighted edge represents a time-dependent correlation between the rates. To provide insights into the clustering of the exchange rate time series, we investigate dynamic communities in the network. We show that there is a relationship between an exchange rate's functional role within the market and its position within its community and use a node-centric community analysis to track the time dynamics of this role. This reveals which exchange rates dominate the market at particular times and also identifies exchange rates that experienced significant changes in market role. We also use the community dynamics to uncover major structural changes that occurred in the FX market. Our techniques are general and will be similarly useful for investigating correlations in other markets.

We consider the problem of fuzzy community detection in networks, which complements and expands the concept of overlapping community structure. Our approach allows each vertex of the graph to belong to multiple communities at the same time, determined by exact numerical membership degrees, even in the presence of uncertainty in the data being analyzed. We create an algorithm for determining the optimal membership degrees with respect to a given goal function. Based on the membership degrees, we introduce a measure that is able to identify outlier vertices that do not belong to any of the communities, bridge vertices that have significant membership in more than one single community, and regular vertices that fundamentally restrict their interactions within their own community, while also being able to quantify the centrality of a vertex with respect to its dominant community. The method can also be used for prediction in case of uncertainty in the data set analyzed. The number of communities can be given in advance, or determined by the algorithm itself, using a fuzzified variant of the modularity function. The technique is able to discover the fuzzy community structure of different real world networks including, but not limited to, social networks, scientific collaboration networks, and cortical networks, with high confidence. 1 Bridges described in this paper are not to be confused with the concept of cut edges, which are sometimes also referred as bridges in classical graph theory. Articulation points ͑vertices whose removal disconnects the remaining subgraph͒ bear more similarity to the concept of bridges described in this paper, but not all bridge vertices are articulation points. From the structural perspective, the concept of bridge and bridgeness may be considered as a generalization of the notion of the articulation point, suitably tailored to the problem of community detection.

Purpose - Constant question in determination of a social system state is how to obtain a sufficient quantity of information with a small enough, manageable indicator set.

Design/methodology/approach - The concept is developed in a thorough analysis of strengths and weaknesses of existing approaches, and in thorough contrasting the proposed solutions with these.

Findings - The social free energy and social entropy overcome the gap between the meta-theoretically founded quantities and indicators related to social system state description. The social free energy measures the totality of resources used for social system preservation during changes in environment. The social entropy measures the number of different realisations of a
particular state.

Practical implications - Combination of social free energy and social entropy is on the one hand a set of quantities easily determinable from available data, and on the other hand a set of indicators ntuitively connected with social system states. We relate the system social free energy and levels of organisation and adaptation. From these measures we derive the measure of social system adaptation.

Originality/value - The use of social free energy in describing the social system states adds a significant value in forming the small set of representative indicators. It is of importance for all researchers; theoreticians and practitioners.

Keywords - Chaos theory, Social systems, Adaptability

We present SNAP (Small-world Network Analysis and Partitioning), an open-source graph framework for exploratory study and partitioning of large-scale networks. To illustrate the capability of SNAP, we discuss the design, implementation, and performance of three novel parallel community detection algorithms that optimize modularity, a popular measure for clustering quality in social network analysis. In order to achieve scalable parallel performance, we exploit typical network characteristics of small-world networks, such as the low graph diameter, sparse connectivity, and skewed degree distribution. We conduct an extensive experimental study on real-world graph instances and demonstrate that our parallel schemes, coupled with aggressive algorithm engineering for smallworld networks, give significant running time improvements over existing modularity-based clustering heuristics, with little or no loss in clustering quality. For instance, our divisive clustering approach based on approximate edge betweenness centrality is more than two orders of magnitude faster than a competing greedy approach, for a variety of large graph instances on the Sun Fire T2000 multicore system. SNAP also contains parallel implementations of fundamental graph-theoretic kernels and topological analysis metrics (e.g., breadth-first search, connected components, vertex and edge centrality) that are optimized for smallworld networks. The SNAP framework is extensible; the graph kernels are modular, portable across shared memory multicore and symmetric multiprocessor systems, and simplify the design of high-level domain-specific applications.

The NETMINE framework allows the characterization of traffic data by means of data mining techniques. NETMINE performs generalized association rule extraction to profile communications, detect anomalies, and identify recurrent patterns. Association rule extraction is a widely used exploratory technique to discover hidden correlations among data. However, it is usually driven by frequency constraints on the extracted correlations. Hence, it entails (i) generating a huge number of rules which are difficult to analyze, or (ii) pruning rare itemsets even if their hidden knowledge might be relevant. To overcome these issues NET-MINE exploits a novel algorithm to efficiently extract generalized association rules, which provide a high level abstraction of the network traffic and allows the discovery of unexpected and more interesting traffic rules. The proposed technique exploits (user provided) taxonomies to drive the pruning phase of the extraction process. Extracted correlations are automatically aggregated in more general association rules according to a frequency threshold. Eventually, extracted rules are classified into groups according to their semantic meaning, thus allowing a domain expert to focus on the most relevant patterns. Experiments performed on different network dumps showed the efficiency and effectiveness of the NETMINE framework to characterize traffic data.

Community detection methods from complex network theory are applied to a subset of the Myspace artist network to identify groups of similar artists. Methods based on the greedy optimization of modularity and random walks are used. In a second iteration, inter-artist audio-based similarity scores are used as input to enhance these community detection methods. The resulting community structures are evaluated using a collection of artist-assigned genre tags. Evidence suggesting the Myspace artist network structure is closely related to musical genre is presented and a Semantic Web service for accessing this structure is described.

Freshwater classification according to the Water Framework Directive (WFD) is based on estimation of the deviation between biological elements found on river stretch in comparison with communities detected in the same river type under reference conditions (undisturbed and near-natural habitats). We present analyses to describe macrophyte development in pristine lowland rivers and to reveal the variation among various stream types. The study is based on a country-wide survey of Poland with a dataset of 642 sites on 367 water courses. Surveyed rivers covered the whole lowland area of Poland. Field surveys were conducted using the Polish macrophyte approach, which enabled calculation of several macrophyte metrics. Firstly, a numerical index MIR (Macrophyte Index for Rivers) was computed, which reflects river degradation, especially eutrophication. Furthermore, five diversity metrics were calculated. Each survey, in addition to macrophyte assessment, was supplemented by a complex suite of environmental records. Reference conditions were defined using four criteria: (1) catchment land use, (2) hydromorphological features, (3) water quality and (4) biological assessment. The selected reference lowland rivers included 40 sites. To classify plant data, two-way indicator species analysis Twinspan was used. This resulted in distinguishing four endclusters which were heterogeneous according to plant composition: organic rivers and three types of siliceous rivers (small with sandy substrate, small with stony substrate and large rivers). The differentiation of environmental factors between river types was confirmed by a variance analysis (ANOVA). Furthermore, the environmental database was explored with principal component analysis (PCA). The PCA principal components were analysed against river types with the canonical correspondence analysis (CCA). Finally botanical differences between identified river types have been detected, using botanical metrics the share of different macrophyte groups and relationships with particular species were defined.