Graph clustering with network structure indices

Authors:
Matthew J. Rattigan;Marc Maier;David Jensen
Affiliations:
University of Massachusetts Amherst;University of Massachusetts Amherst;University of Massachusetts Amherst
Venue:
Proceedings of the 24th international conference on Machine learning
Year:
2007

Citing 5
Cited 11

Matrix multiplication via arithmetic progressions

STOC '87 Proceedings of the nineteenth annual ACM symposium on Theory of computing
A Machine Learning Approach to Building Domain-Specific Search Engines

IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Kernel k-means: spectral clustering and normalized cuts

Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Using structure indices for efficient approximation of network properties

Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Clustering graphs for visualization via node similarities

Journal of Visual Languages and Computing

A measure for cluster cohesion in semantic overlay networks

Proceedings of the 2008 ACM workshop on Large-Scale distributed systems for information retrieval
Robustness of emerged community in social network

Proceedings of the International Conference on Management of Emergent Digital EcoSystems
Efficient social network approximate analysis on blogosphere based on network structure characteristics

Proceedings of the 3rd Workshop on Social Network Mining and Analysis
Clustering dense graphs: A web site graph paradigm

Information Processing and Management: an International Journal
Information theoretic criteria for community detection

SNAKDD'08 Proceedings of the Second international conference on Advances in social network mining and analysis
Affinity propagation on identifying communities in social and biological networks

KSEM'10 Proceedings of the 4th international conference on Knowledge science, engineering and management
Indexing Network Structure with Shortest-Path Trees

ACM Transactions on Knowledge Discovery from Data (TKDD)
Shortest-path queries for complex networks: exploiting low tree-width outside the core

Proceedings of the 15th International Conference on Extending Database Technology
Frequent conceptual links and link-based clustering: a comparative analysis of two clustering techniques

Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Efficient community detection with additive constrains on large networks

Knowledge-Based Systems
Shortest-path queries in static networks

ACM Computing Surveys (CSUR)

Quantified Score

Hi-index	0.00

Visualization

Abstract

Graph clustering has become ubiquitous in the study of relational data sets. We examine two simple algorithms: a new graphical adaptation of the k-medoids algorithm and the Girvan-Newman method based on edge betweenness centrality. We show that they can be effective at discovering the latent groups or communities that are defined by the link structure of a graph. However, both approaches rely on prohibitively expensive computations, given the size of modern relational data sets. Network structure indices (NSIs) are a proven technique for indexing network structure and efficiently finding short paths. We show how incorporating NSIs into these graph clustering algorithms can overcome these complexity limitations. We also present promising quantitative and qualitative evaluations of the modified algorithms on synthetic and real data sets.