Finding Strongly Knit Clusters in Social Networks
Reads0
Chats0
TLDR
This paper introduces a new criterion that overcomes limitations by combining internal density with external sparsity in a natural way, and explores combinatorial properties of internally dense and externally sparse clusters.Abstract:
Social networks are ubiquitous The discovery of close-knit clusters in these networks is of fundamental and practical interest Existing clustering criteria are limited in that clusters typically do not overlap, all vertices are clustered and/or external sparsity is ignored We introduce a new criterion that overcomes these limitations by combining internal density with external sparsity in a natural way This paper explores combinatorial properties of internally dense and externally sparse clusters A simple algorithm is given for provably finding such clusters assuming a sufficiently large gap between internal density and e sparsity Experiments show that the algorithm is able to identify over 90% of the clusters in real graphs, assuming conditions on external sparsityread more
Citations
More filters
Journal ArticleDOI
A Survey of Statistical Network Models
TL;DR: In this paper, the authors provide an overview of the historical development of statistical network modeling and then introduce a number of examples that have been studied in the network literature and their subsequent discussion focuses on some prominent static and dynamic network models and their interconnections.
Proceedings ArticleDOI
Mining structural hole spanners through information diffusion in social networks
Tiancheng Lou,Jie Tang +1 more
TL;DR: This work precisely defines the problem of mining top-k structural hole spanners in large-scale social networks and provides an objective (quality) function to formalize the problem and proposes an efficient algorithm with provable approximation guarantees to solve the problem.
Proceedings ArticleDOI
Scalable Motif-aware Graph Clustering
TL;DR: In this article, the authors developed new methods based on graph motifs for graph clustering, allowing more efficient detection of communities within networks, focusing on triangles within graphs but their techniques extend to other clique motifs as well.
Proceedings ArticleDOI
Efficient Densest Subgraph Computation in Evolving Graphs
TL;DR: This work studies the densest subgraph problem in the the dynamic graph model, for which it is presented the first scalable algorithm with provable guarantees, and shows that (approximate) densmost subgraphs can be maintained efficiently within hundred of microseconds per update.
Book
Scalable Algorithms for Data and Network Analysis
TL;DR: This tutorial surveys a family of algorithmic techniques for the design of provably-good scalable algorithms and illustrates the use of these techniques by a few basic problems that are fundamental in network analysis, particularly for the identification of significant nodes and coherent clusters/communities insocial and information networks.
References
More filters
Journal ArticleDOI
Normalized cuts and image segmentation
Jianbo Shi,Jitendra Malik +1 more
TL;DR: This work treats image segmentation as a graph partitioning problem and proposes a novel global criterion, the normalized cut, for segmenting the graph, which measures both the total dissimilarity between the different groups as well as the total similarity within the groups.
Proceedings ArticleDOI
Normalized cuts and image segmentation
Jianbo Shi,Jitendra Malik +1 more
TL;DR: This work treats image segmentation as a graph partitioning problem and proposes a novel global criterion, the normalized cut, for segmenting the graph, which measures both the total dissimilarity between the different groups as well as the total similarity within the groups.
Journal ArticleDOI
Modularity and community structure in networks
TL;DR: In this article, the modularity of a network is expressed in terms of the eigenvectors of a characteristic matrix for the network, which is then used for community detection.
Journal ArticleDOI
Uncovering the overlapping community structure of complex networks in nature and society
TL;DR: After defining a set of new characteristic quantities for the statistics of communities, this work applies an efficient technique for exploring overlapping communities on a large scale and finds that overlaps are significant, and the distributions introduced reveal universal features of networks.
Journal ArticleDOI
Trawling the Web for emerging cyber-communities
TL;DR: The subject of this paper is the systematic enumeration of over 100,000 emerging communities from a Web crawl, motivating a graph-theoretic approach to locating such communities, and describing the algorithms and algorithmic engineering necessary to find structures that subscribe to this notion.