Erratum: inverting a sum of matrices
SIAM Review
Inferring Web communities from link topology
Proceedings of the ninth ACM conference on Hypertext and hypermedia : links, objects, time and space---structure in hypermedia systems: links, objects, time and space---structure in hypermedia systems
Multilevel k-way hypergraph partitioning
Proceedings of the 36th annual ACM/IEEE Design Automation Conference
Proceedings of the 9th international World Wide Web conference on Computer networks : the international journal of computer and telecommunications netowrking
Maximizing the spread of influence through a social network
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
The link prediction problem for social networks
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Fast discovery of connection subgraphs
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Automatic multimedia cross-modal correlation discovery
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Manifold-ranking based image retrieval
Proceedings of the 12th annual ACM international conference on Multimedia
On mining cross-graph quasi-cliques
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Using relational knowledge discovery to prevent securities fraud
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Discovering frequent topological structures from graph datasets
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Mining compressed frequent-pattern sets
VLDB '05 Proceedings of the 31st international conference on Very large data bases
Group formation in large social networks: membership, growth, and evolution
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Measuring and extracting proximity in networks
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Center-piece subgraphs: problem definition and fast solutions
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Predictive discrete latent factor models for large scale dyadic data
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Fast best-effort pattern matching in large attributed graphs
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
BANKS: browsing and keyword searching in relational databases
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Objectrank: authority-based keyword search in databases
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Random walk with restart: fast solutions and applications
Knowledge and Information Systems
Recommendation via Query Centered Random Walk on K-Partite Graph
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Simultaneously Finding Fundamental Articles and New Topics Using a Community Tracking Method
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
| Hi-index | 0.00 |
Given a graph, how to find a small group of `gateways', that is a small subset of nodes that are crucial in connecting the source to the target? For instance, given a social network, who is the best person to introduce you to, say, Chris Ferguson, the poker champion? Or, given a network of people and skills, who is the best person to help you learn about, say, wavelets? We formally formulate this problem in two scenarios: Pair-Gateway and Group-Gateway. For each scenario, we show that it is sub-modular and thus it can be solved near-optimally. We further give fast, scalable algorithms to find such gateways. Extensive experimental evaluations on real data sets demonstrate the effectiveness and efficiency of the proposed methods.