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Structuring Peer-to-Peer Networks Using Interest-Based Communities
[chapter]
2004
Lecture Notes in Computer Science
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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
2015
International Journal of Computer Applications
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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]
2021
arXiv
pre-print
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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]
2018
arXiv
pre-print
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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
2016
IEEE Transactions on Visualization and Computer Graphics
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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
2015
Physics Procedia
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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
CL-SNA
2009
Proceedings of the 2007 International Lisp Conference on - ILC '07
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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]
2015
Proceedings of the 2015 SIAM International Conference on Data Mining
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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
2010
Advances in Data Analysis and Classification
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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
2009
IEEE Internet Computing
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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]
2011
Social Network Data Analytics
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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
2017
EAI Endorsed Transactions on Scalable Information Systems
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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
2014
Proceedings of the 7th ACM international conference on Web search and data mining - WSDM '14
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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
2021
SN Computer Science
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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
2007
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining - KDD '07
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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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