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Structuring Peer-to-Peer Networks Using Interest-Based Communities [chapter]

Mujtaba Khambatti, Kyung Dong Ryu, Partha Dasgupta
2004 Lecture Notes in Computer Science  
In this paper, we introduce the notion of peer communities. Communities are like interest groups, modeled after human communities and can overlap.  ...  Our work focuses on providing efficient formation, discovery and management techniques that can be implemented to constantly changing community structures.  ...  similar solutions in social networks.  ... 
doi:10.1007/978-3-540-24629-9_5 fatcat:tlsywefqnjb4veq7ykbzceg73u

A New Fuzzy based Approach for Destabilization of Terrorist Network

Suraksha Tiwari, Shilky Shrivastava, Manish Gupta
2015 International Journal of Computer Applications  
Recently, Terrorist Network Mining (special branch of Social Network Analysis) has gained considerable prominence in the data mining community because of its relevance to the real scenario situation and  ...  The wide scope of Social network analysis has led the law require agencies to study the performance of social network.  ...  IDM borrows ideas from social network analysis and the graph theory methods in the order to connect the nodes and assist law enforcement agencies to the terrorist networks disconnect [17] .  ... 
doi:10.5120/ijca2015907068 fatcat:4frk24bkanhapgccgxx5excije

Fake Reviewer Group Detection in Online Review Systems [article]

Chen Cao, Shihao Li, Shuo Yu, Zhikui Chen
2021 arXiv   pre-print
First, cohensive groups are detected with modularity-based graph convolutional networks.  ...  In this work, we present an unsupervised and end-to-end approach for fake reviewer group detection in online reviews. Specifically, our method can be summarized into two procedures.  ...  Graph Neural Networks candidate group.  ... 
arXiv:2112.06403v1 fatcat:vf6ku3uezva23fuoi6kohnooay

Graph Summarization Methods and Applications: A Survey [article]

Yike Liu, Tara Safavi, Abhilash Dighe, Danai Koutra
2018 arXiv   pre-print
Finally, we discuss applications of summarization on real-world graphs and conclude by describing some open problems in the field.  ...  This survey is a structured, comprehensive overview of the state-of-the-art methods for summarizing graph data. We first broach the motivation behind, and the challenges of, graph summarization.  ...  to obtain compact representations of graphs with attributes.  ... 
arXiv:1612.04883v3 fatcat:fhg2g5eldfdgfkzoqdmbfl5er4

Adaptive Disentanglement Based on Local Clustering in Small-World Network Visualization

Arlind Nocaj, Mark Ortmann, Ulrik Brandes
2016 IEEE Transactions on Visualization and Computer Graphics  
Recent approaches thus aim at finding a sparser representation of the graph to amplify variations in pairwise distances.  ...  The approach is based on an empirical relationship between input and output characteristics that is derived from real and synthetic networks.  ...  ACKNOWLEDGMENTS The authors thank the German Research Foundation (DFG) for financial support under grant Br 2158/11-1 and within project B02 of SFB/Transregio 161.  ... 
doi:10.1109/tvcg.2016.2534559 pmid:26955036 fatcat:g7ojmzykkbddxn4fw6ksw3sch4

From Patterns in Data to Knowledge Discovery: What Data Mining Can Do

Francesco Gullo
2015 Physics Procedia  
Data mining is defined as the computational process of analyzing large amounts of data in order to extract patterns and useful information.  ...  In this technical note we provide a high-level overview of the most prominent tasks and methods that form the basis of data mining.  ...  This technical note provided a broad overview of the main data-mining principles and its interdisciplinary aspects.  ... 
doi:10.1016/j.phpro.2015.02.005 fatcat:dbtp3fk4ifdy3f3zv7wz6dbolm


Mehmet Gencer, Coskun Gunduz, Vehbi Sinan Tunalioglu
2009 Proceedings of the 2007 International Lisp Conference on - ILC '07  
Group level measures: Discovery and comparison of structural subgroups in a social group has been very interesting for social scientists.  ...  However, even Graphviz cannot always automatically generate well-balanced, compact visualization of social network data.  ... 
doi:10.1145/1622123.1622143 dblp:conf/lfp/GencerGT07 fatcat:cwi3uu4edzedhjiukl6zaivuhe

Efficient Algorithms for a Robust Modularity-Driven Clustering of Attributed Graphs [chapter]

Patricia Iglesias Sánchez, Emmanuel Müller, Uwe Leo Korn, Klemens Böhm, Andrea Kappes, Tanja Hartmann, Dorothea Wagner
2015 Proceedings of the 2015 SIAM International Conference on Data Mining  
In this work, we focus on the robustness of graph clustering w.r.t. irrelevant attributes and outliers.  ...  In our experiments, we evaluate our modularity-driven algorithms w.r.t. the new challenges in attributed graphs and show that they outperform existing approaches on large attributed graphs.  ...  Introduction A wide range of applications in industry and sciences use graph clustering for knowledge discovery.  ... 
doi:10.1137/1.9781611974010.12 dblp:conf/sdm/SanchezMKBKHW15 fatcat:2wxsxlhka5ezffytbsntqyuqeu

Network ensemble clustering using latent roles

Ulrik Brandes, Jürgen Lerner, Uwe Nagel
2010 Advances in Data Analysis and Classification  
This approach is motivated by social network concepts, and we demonstrate its utility on an ensemble of personal networks of migrants, where we extract structurally similar groups and show their resemblance  ...  Given a network ensemble (a collection of attributed graphs with some substantive commonality), we start by partitioning the set of all vertices based on attribute similarity.  ...  We thank the anonymous reviewers and the editors for their helpful comments.  ... 
doi:10.1007/s11634-010-0074-3 fatcat:ss7cxdqbyfgejm3tklghmdecni

Semantic Technology for Capturing Communication Inside an Organization

Marko Grobelnik, Dunja Mladenic, Blaž Fortuna
2009 IEEE Internet Computing  
We conclude that the proposed approach is useful and applicable in real life situations where the goal is to model social structures based on communication records.  ...  As an example we use social network of a mid size research institution obtained based on e-mail communication.  ...  In the proposed approach we replace lexical items by nodes of the social network and describe each node by its context in the social network graph.  ... 
doi:10.1109/mic.2009.88 fatcat:qb25drqosjfkvjwgn7x4y5lptu

Community Discovery in Social Networks: Applications, Methods and Emerging Trends [chapter]

S. Parthasarathy, Y. Ruan, V. Satuluri
2011 Social Network Data Analytics  
This is particularly true in the social context, especially given recent advances in Internet technologies and Web 2.0 applications leading to a diverse range of evolving social networks.  ...  Analysis of such networks can result in the discovery of important patterns and potentially shed light on important properties governing the growth of such networks.  ...  This work is supported in part by the following grants: NSF CAREER IIS-0347662, CCF-0702587, IIS-0917070.  ... 
doi:10.1007/978-1-4419-8462-3_4 fatcat:lsvhj6terjbjzpkjoobdobp4gq

An Unsupervised Approach of Knowledge Discovery from Big Data in Social Network

Mohiuddin Ahmed
2017 EAI Endorsed Transactions on Scalable Information Systems  
Social network is a common source of big data. It is becoming increasingly difficult to understand what is happening in the network due to the volume.  ...  In this paper, the effectiveness co-clustering is explored to create meaningful summary of social network data such as Twitter.  ...  For example, in social network datasets, the columns are composed of different features but there are group of features which are similar and can be considered as a representative of that group.  ... 
doi:10.4108/eai.25-9-2017.153148 fatcat:g3uvluxqw5gr3ewtn3lq6oa6zq

Spatial compactness meets topical consistency

Mrinmaya Sachan, Avinava Dubey, Shashank Srivastava, Eric P. Xing, Eduard Hovy
2014 Proceedings of the 7th ACM international conference on Web search and data mining - WSDM '14  
In this paper, we address the problem of discovering topically meaningful, yet compact (densely connected) communities in a social network.  ...  Assuming the social network to be an integer-weighted graph (where the weights can be intuitively defined as the number of common friends, followers, documents exchanged, etc.), we transform the social  ...  Acknowledgments Thanks to Qirong Ho, Kriti Puniyani, Keerthiram Murugesan and the anonymous reviewers for their valuable comments and suggestions to improve the quality of the paper. A. REFERENCES  ... 
doi:10.1145/2556195.2556219 dblp:conf/wsdm/SachanDSXH14 fatcat:tnty5i4s5ffkfjnafnhaqon2uq

Group Anomaly Detection: Past Notions, Present Insights, and Future Prospects

Aqeel Feroze, Ali Daud, Tehmina Amjad, Malik Khizar Hayat
2021 SN Computer Science  
Anomaly detection has evolved as a successful research subject in the areas such as bibliometrics, informatics and computer networks including security-based and social networks.  ...  In this research, we bifurcated group anomaly detection techniques into activity-based and graph-based methods.  ...  in bibliographic citation data and academic social network, using a labeled graph structure.  ... 
doi:10.1007/s42979-021-00603-x fatcat:oyjzthza7vbhnakpm3t2ko6ctq

Joint cluster analysis of attribute and relationship data withouta-priori specification of the number of clusters

Flavia Moser, Rong Ge, Martin Ester
2007 Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '07  
True clusters are assumed to be compact and distinctive from their neighboring clusters in terms of attribute data and internally connected in terms of relationship data.  ...  In such cases, a joint analysis of both types of data can yield more accurate results than classical clustering algorithms that either use only attribute data or only relationship (graph) data.  ...  As motivated in the introduction, social network analysis and hotspot analysis require the consideration of attribute and relationship data.  ... 
doi:10.1145/1281192.1281248 dblp:conf/kdd/MoserGE07 fatcat:roxjrisvnnc2rgsmlr7azwkw2e
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