Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Identifying Communities of Practice through Ontology Network Analysis
IEEE Intelligent Systems
Convex Optimization
Finding community structure in mega-scale social networks: [extended abstract]
Proceedings of the 16th international conference on World Wide Web
A spectral clustering approach to optimally combining numericalvectors with a modular network
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
SCAN: a structural clustering algorithm for networks
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Statistical properties of community structure in large social and information networks
Proceedings of the 17th international conference on World Wide Web
Efficient aggregation for graph summarization
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
ACM Transactions on Knowledge Discovery from Data (TKDD)
Developing a feature weight self-adjustment mechanism for a K-means clustering algorithm
Computational Statistics & Data Analysis
Community evolution in dynamic multi-mode networks
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
MetaFac: community discovery via relational hypergraph factorization
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Graph clustering based on structural/attribute similarities
Proceedings of the VLDB Endowment
Empirical comparison of algorithms for network community detection
Proceedings of the 19th international conference on World wide web
SHRINK: a structural clustering algorithm for detecting hierarchical communities in networks
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
A novel similarity-based modularity function for graph partitioning
DaWaK'07 Proceedings of the 9th international conference on Data Warehousing and Knowledge Discovery
Identifying Overlying Group of People through Clustering
International Journal of Information Technology and Web Engineering
A general collaborative filtering framework based on matrix bordered block diagonal forms
Proceedings of the 24th ACM Conference on Hypertext and Social Media
Improve collaborative filtering through bordered block diagonal form matrices
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Solving the missing node problem using structure and attribute information
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
TODMIS: mining communities from trajectories
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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With the recent advances in information networks, the problem of community detection has attracted much attention in the last decade. While network community detection has been ubiquitous, the task of collecting complete network data remains challenging in many real-world applications. Usually the collected network is incomplete with most of the edges missing. Commonly, in such networks, all nodes with attributes are available while only the edges within a few local regions of the network can be observed. In this paper, we study the problem of detecting communities in incomplete information networks with missing edges. We first learn a distance metric to reproduce the link-based distance between nodes from the observed edges in the local information regions. We then use the learned distance metric to estimate the distance between any pair of nodes in the network. A hierarchical clustering approach is proposed to detect communities within the incomplete information networks. Empirical studies on real-world information networks demonstrate that our proposed method can effectively detect community structures within incomplete information networks.