Matrix analysis
SimRank: a measure of structural-context similarity
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Iterative Methods for Sparse Linear Systems
Iterative Methods for Sparse Linear Systems
Link fusion: a unified link analysis framework for multi-type interrelated data objects
Proceedings of the 13th international conference on World Wide Web
MRSSA: an iterative algorithm for similarity spreading over interrelated objects
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Matrix Analysis For Scientists And Engineers
Matrix Analysis For Scientists And Engineers
SimFusion: measuring similarity using unified relationship matrix
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
Linear Algebra With Applications
Linear Algebra With Applications
P-Rank: a comprehensive structural similarity measure over information networks
Proceedings of the 18th ACM conference on Information and knowledge management
Accuracy estimate and optimization techniques for SimRank computation
The VLDB Journal — The International Journal on Very Large Data Bases
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
A space and time efficient algorithm for SimRank computation
World Wide Web
ASCOS: an asymmetric network structure COntext similarity measure
Proceedings of the 2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining
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SimFusion has become a captivating measure of similarity between objects in a web graph. It is iteratively distilled from the notion that "the similarity between two objects is reinforced by the similarity of their related objects". The existing SimFusion model usually exploits the Unified Relationship Matrix (URM) to represent latent relationships among heterogeneous data, and adopts an iterative paradigm for SimFusion computation. However, due to the row normalization of URM, the traditional SimFusion model may produce the trivial solution; worse still, the iterative computation of SimFusion may not ensure the global convergence of the solution. This paper studies the revision of this model, providing a full treatment from complexity to algorithms. (1) We propose SimFusion+ based on a notion of the Unified Adjacency Matrix (UAM), a modification of the URM, to prevent the trivial solution and the divergence issue of SimFusion. (2) We show that for any vertex-pair, SimFusion+ can be performed in O(1) time and O(n) space with an O(km)-time precomputation done only once, as opposed to the O(kn3) time and O(n2) space of its traditional counterpart, where n, m, and k denote the number of vertices, edges, and iterations respectively. (3) We also devise an incremental algorithm for further improving the computation of SimFusion+ when networks are dynamically updated, with performance guarantees for similarity estimation. We experimentally verify that these algorithms scale well, and the revised notion of SimFusion is able to converge to a non-trivial solution, and allows us to identify more sensible structure information in large real-world networks.