Automatic text processing: the transformation, analysis, and retrieval of information by computer
Automatic text processing: the transformation, analysis, and retrieval of information by computer
The Stanford GraphBase: a platform for combinatorial computing
The Stanford GraphBase: a platform for combinatorial computing
A distributed memory unstructured gauss-seidel algorithm for multigrid smoothers
Proceedings of the 2001 ACM/IEEE conference on Supercomputing
Lanczos Algorithms for Large Symmetric Eigenvalue Computations, Vol. 1
Lanczos Algorithms for Large Symmetric Eigenvalue Computations, Vol. 1
SimRank: a measure of structural-context similarity
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
SimFusion: measuring similarity using unified relationship matrix
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Introduction to Data Mining, (First Edition)
Introduction to Data Mining, (First Edition)
Contextual search and name disambiguation in email using graphs
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
The link-prediction problem for social networks
Journal of the American Society for Information Science and Technology
P-Rank: a comprehensive structural similarity measure over information networks
Proceedings of the 18th ACM conference on Information and knowledge management
Link Prediction on Evolving Data Using Matrix and Tensor Factorizations
ICDMW '09 Proceedings of the 2009 IEEE International Conference on Data Mining Workshops
Fast computation of SimRank for static and dynamic information networks
Proceedings of the 13th International Conference on Extending Database Technology
Closed form solution of similarity algorithms
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Parallel SimRank computation on large graphs with iterative aggregation
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
CollabSeer: a search engine for collaboration discovery
Proceedings of the 11th annual international ACM/IEEE joint conference on Digital libraries
Capturing missing edges in social networks using vertex similarity
Proceedings of the sixth international conference on Knowledge capture
Applying Link Prediction to Ranking Candidates for High-Level Government Post
ASONAM '11 Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining
Link Prediction Based on Local Information
ASONAM '11 Proceedings of the 2011 International Conference on Advances in Social Networks Analysis and Mining
How user behavior is related to social affinity
Proceedings of the fifth ACM international conference on Web search and data mining
Discovering missing links in networks using vertex similarity measures
Proceedings of the 27th Annual ACM Symposium on Applied Computing
Efficient personalized pagerank with accuracy assurance
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
SimFusion+: extending simfusion towards efficient estimation on large and dynamic networks
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
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Discovering similar objects in a social network has many interesting issues. Here, we present ASCOS, an Asymmetric Structure COntext Similarity measure that captures the similarity scores among any pairs of nodes in a network. The definition of ASCOS is similar to that of the well-known SimRank since both define score values recursively. However, we show that ASCOS outputs a more complete similarity score than SimRank because SimRank (and several of its variations, such as P-Rank and SimFusion) on average ignores half paths between nodes during calculation. To make ASCOS tractable in both computation time and memory usage, we propose two variations of ASCOS: a low rank approximation based approach and an iterative solver Gauss-Seidel for linear equations. When the target network is sparse, the run time and the required computing space of these variations are smaller than computing SimRank and ASCOS directly. In addition, the iterative solver divides the original network into several independent sub-systems so that a multi-core server or a distributed computing environment, such as MapReduce, can efficiently solve the problem. We compare the performance of ASCOS with other global structure based similarity measures, including SimRank, Katz, and LHN. The experimental results based on user evaluation suggest that ASCOS gives better results than other measures. In addition, the asymmetric property has the potential to identify the hierarchical structure of a network. Finally, variations of ASCOS (including one distributed variation) can also reduce computation both in space and time.