Automatic subspace clustering of high dimensional data for data mining applications
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Inferring Web communities from link topology
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Probabilistic latent semantic indexing
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Normalized Cuts and Image Segmentation
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Efficient and Effective Clustering Methods for Spatial Data Mining
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SimRank: a measure of structural-context similarity
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Fast discovery of connection subgraphs
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A cross-collection mixture model for comparative text mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining hidden community in heterogeneous social networks
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Center-piece subgraphs: problem definition and fast solutions
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast Random Walk with Restart and Its Applications
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GraphScope: parameter-free mining of large time-evolving graphs
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
SCAN: a structural clustering algorithm for networks
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Graph summarization with bounded error
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
Efficient aggregation for graph summarization
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Developing a feature weight self-adjustment mechanism for a K-means clustering algorithm
Computational Statistics & Data Analysis
Spotting Significant Changing Subgraphs in Evolving Graphs
ICDM '08 Proceedings of the 2008 Eighth IEEE International Conference on Data Mining
RankClus: integrating clustering with ranking for heterogeneous information network analysis
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Structural correlation pattern mining for large graphs
Proceedings of the Eighth Workshop on Mining and Learning with Graphs
Taming computational complexity: efficient and parallel simrank optimizations on undirected graphs
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Structure and attribute index for approximate graph matching in large graphs
Information Systems
Graph cube: on warehousing and OLAP multidimensional networks
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Assessing and ranking structural correlations in graphs
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K-means based approaches to clustering nodes in annotated graphs
ISMIS'11 Proceedings of the 19th international conference on Foundations of intelligent systems
Improving the accuracy of similarity measures by using link information
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Is there a best quality metric for graph clusters?
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DB-CSC: a density-based approach for subspace clustering in graphs with feature vectors
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ASAP: towards accurate, stable and accelerative penetrating-rank estimation on large graphs
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Efficient name disambiguation in digital libraries
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Collective prediction with latent graphs
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On clustering heterogeneous social media objects with outlier links
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Relation strength-aware clustering of heterogeneous information networks with incomplete attributes
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Fast and exact top-k search for random walk with restart
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Mining attribute-structure correlated patterns in large attributed graphs
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Fuse: towards multi-level functional summarization of protein interaction networks
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A space and time efficient algorithm for SimRank computation
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Discovering collective viewpoints on micro-blogging events based on community and temporal aspects
ADMA'11 Proceedings of the 7th international conference on Advanced Data Mining and Applications - Volume Part I
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SIGMOD '12 Proceedings of the 2012 ACM SIGMOD International Conference on Management of Data
Collective viewpoint identification of low-level participation
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
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DASFAA'12 Proceedings of the 17th international conference on Database Systems for Advanced Applications
On supervised mining of dynamic content-based networks1
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PAKDD'12 Proceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Measuring two-event structural correlations on graphs
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SSDBM'12 Proceedings of the 24th international conference on Scientific and Statistical Database Management
Evolution of social-attribute networks: measurements, modeling, and implications using google+
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Online community detection in social sensing
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On Finding Fine-Granularity User Communities by Profile Decomposition
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Social-Based Conceptual Links: Conceptual Analysis Applied to Social Networks
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Linked data classification: a feature-based approach
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IRWR: incremental random walk with restart
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RMiCS: a robust approach for mining coherent subgraphs in edge-labeled multi-layer graphs
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Structure and attributes community detection benchmark and a novel selection method
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
Understanding evolving group structures in time-varying networks
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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From Frequent Features to Frequent Social Links
International Journal of Information System Modeling and Design
Evaluating community detection using a bi-objective optimization
ICIC'13 Proceedings of the 9th international conference on Intelligent Computing Theories
Identification of collective viewpoints on microblogs
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Security of graph data: hashing schemes and definitions
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User profiling in an ego network: co-profiling attributes and relationships
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Name disambiguation in scientific cooperation network by exploiting user feedback
Artificial Intelligence Review
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The goal of graph clustering is to partition vertices in a large graph into different clusters based on various criteria such as vertex connectivity or neighborhood similarity. Graph clustering techniques are very useful for detecting densely connected groups in a large graph. Many existing graph clustering methods mainly focus on the topological structure for clustering, but largely ignore the vertex properties which are often heterogenous. In this paper, we propose a novel graph clustering algorithm, SA-Cluster, based on both structural and attribute similarities through a unified distance measure. Our method partitions a large graph associated with attributes into k clusters so that each cluster contains a densely connected subgraph with homogeneous attribute values. An effective method is proposed to automatically learn the degree of contributions of structural similarity and attribute similarity. Theoretical analysis is provided to show that SA-Cluster is converging. Extensive experimental results demonstrate the effectiveness of SA-Cluster through comparison with the state-of-the-art graph clustering and summarization methods.