Colibri: fast mining of large static and dynamic graphs
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Graph nodes clustering with the sigmoid commute-time kernel: A comparative study
Data & Knowledge Engineering
TANGENT: a novel, 'Surprise me', recommendation algorithm
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
iPoG: fast interactive proximity querying on graphs
Proceedings of the 18th ACM conference on Information and knowledge management
A document-sensitive graph model for multi-document summarization
Knowledge and Information Systems
Optimal rare query suggestion with implicit user feedback
Proceedings of the 19th international conference on World wide web
Metric forensics: a multi-level approach for mining volatile graphs
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast sparse matrix-vector multiplication on GPUs: implications for graph mining
Proceedings of the VLDB Endowment
Disease gene prioritization based on topological similarity in protein-protein interaction networks
RECOMB'11 Proceedings of the 15th Annual international conference on Research in computational molecular biology
Post-ranking query suggestion by diversifying search results
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Tagging image by exploring weighted correlation between visual features and tags
WAIM'11 Proceedings of the 12th international conference on Web-age information management
Finding appropriate experts for collaboration
WAIM'11 Proceedings of the 12th international conference on Web-age information management
Random walk based entity ranking on graph for multidimensional recommendation
Proceedings of the fifth ACM conference on Recommender systems
"Tell me more": finding related items from user provided feedback
DS'11 Proceedings of the 14th international conference on Discovery science
GaMuSo: graph base music recommendation in a social bookmarking service
IDA'11 Proceedings of the 10th international conference on Advances in intelligent data analysis X
Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Role of centrality in network-based prioritization of disease genes
EvoBIO'10 Proceedings of the 8th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
BASSET: scalable gateway finder in large graphs
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part II
Fast algorithm for affinity propagation
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
Gateway finder in large graphs: problem definitions and fast solutions
Information Retrieval
Transductive multi-label ensemble classification for protein function prediction
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
SympGraph: a framework for mining clinical notes through symptom relation graphs
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
A group trust metric for identifying people of trust in online social networks
Expert Systems with Applications: An International Journal
Co-transfer learning via joint transition probability graph based method
Proceedings of the 1st International Workshop on Cross Domain Knowledge Discovery in Web and Social Network Mining
Targeted and scalable information dissemination in a distributed reputation mechanism
Proceedings of the seventh ACM workshop on Scalable trusted computing
Exploiting latent relevance for relational learning of ubiquitous things
Proceedings of the 21st ACM international conference on Information and knowledge management
Balanced feature matching in probabilistic framework and its application on object localisation
International Journal of Computer Applications in Technology
Discovering social media experts by integrating social networks and contents
ADC '12 Proceedings of the Twenty-Third Australasian Database Conference - Volume 124
IRWR: incremental random walk with restart
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Correlation discovery in web of things
Proceedings of the 22nd international conference on World Wide Web companion
Predicting the social influence of upcoming contents in large social networks
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
High efficiency and quality: large graphs matching
The VLDB Journal — The International Journal on Very Large Data Bases
Protein Function Prediction using Multi-label Ensemble Classification
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Random walks based modularity: application to semi-supervised learning
Proceedings of the 23rd international conference on World wide web
QuMinS: Fast and scalable querying, mining and summarizing multi-modal databases
Information Sciences: an International Journal
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How closely related are two nodes in a graph? How to compute this score quickly, on huge, disk-resident, real graphs? Random walk with restart (RWR) provides a good relevance score between two nodes in a weighted graph, and it has been successfully used in numerous settings, like automatic captioning of images, generalizations to the “connection subgraphs”, personalized PageRank, and many more. However, the straightforward implementations of RWR do not scale for large graphs, requiring either quadratic space and cubic pre-computation time, or slow response time on queries. We propose fast solutions to this problem. The heart of our approach is to exploit two important properties shared by many real graphs: (a) linear correlations and (b) block-wise, community-like structure. We exploit the linearity by using low-rank matrix approximation, and the community structure by graph partitioning, followed by the Sherman–Morrison lemma for matrix inversion. Experimental results on the Corel image and the DBLP dabasets demonstrate that our proposed methods achieve significant savings over the straightforward implementations: they can save several orders of magnitude in pre-computation and storage cost, and they achieve up to 150 × speed up with 90%+ quality preservation.