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| Content Provider | ACM Digital Library |
|---|---|
| Author | Bogdanov, Petko Singh, Ambuj |
| Abstract | Nearest neighbor proximity search in large graphs is an important analysis primitive with a variety of applications in graph data from different domains. We propose a novel proximity measure for weighted graphs called Effective Importance which incorporates multiple paths between nodes and captures the inherent structural clusters within a network. We develop effective bounds on the EI value using a modified small subnetwork around a query node, enabling scalable exact nearest neighbor (NN) search at query time. Our NN search does not require heavy offline analysis or holistic knowledge of the graph, making our method suitable for very large dynamically changing networks or composite network overlays. We employ our NN search algorithm on social, information and biological networks and demonstrate the effectiveness and scalability of the approach. For million-node networks, our method retrieves the exact top 20 neighbors using less than \$0.2%\$ of the network edges in a fraction of a second on a conventional desktop machine. We also evaluate the effectiveness of our proximity measure and NN search for three applications, namely (i) finding good local clusters, (ii) network sparsification and (iii) prediction of node attributes in information networks. The EI measure and NN search method outperform recent counterparts from the literature in all applications. |
| Starting Page | 1009 |
| Ending Page | 1018 |
| Page Count | 10 |
| File Format | |
| ISBN | 9781450322638 |
| DOI | 10.1145/2505515.2505522 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2013-10-27 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Nearest neighbors Random walks Graph search Sparsification Effective importance |
| Content Type | Text |
| Resource Type | Article |
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