Name
Abstract | ||
|---|---|---|
Many important real-world systems, modeled naturally as complex networks, have heterogeneous interactions and complicated dependency structures. Link prediction in such networks must model the influences between heterogenous relationships and distinguish the formation mechanisms of each link type, a task which is beyond the simple topological features commonly used to score potential links. In this paper, we introduce a novel probabilistically weighted extension of the Adamic/Adar measure for heterogenous information networks, which we use to demonstrate the potential benefits of diverse evidence, particularly in cases where homogeneous relationships are very sparse. However, we also expose some fundamental flaws of traditional a priori link prediction. In accordance with previous research on homogeneous networks, we further demonstrate that a supervised approach to link prediction can enhance performance and is easily extended to the heterogeneous case. Finally, we present results on three diverse, real-world heterogeneous information networks and discuss the trends and tradeoffs of supervised and unsupervised link prediction in a multi-relational setting. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ASONAM.2011.107 | ASONAM |
Keywords | Field | DocType |
link prediction,heterogenous relationship,multi-relational link prediction,diverse evidence,heterogenous information network,heterogeneous information networks,link type,heterogeneous interactions,heterogeneous interaction,multirelational link prediction,probabilistically weighted extension,real-world systems,score potential link,complex networks,heterogeneous case,information networks,supervised approach,unsupervised link prediction,classification,social networking (online),complicated dependency structures,unsupervised learning,real-world heterogeneous information network,adamic-adar measure,proteins,meteorology,system modeling,probabilistic logic,complex network | Data mining,Information networks,Homogeneous,Computer science,A priori and a posteriori,Unsupervised learning,Complex network,Artificial intelligence,Machine learning | Conference |
ISBN | Citations | PageRank |
978-0-7695-4375-8 | 52 | 1.72 |
References | Authors | |
9 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Darcy Davis | 1 | 165 | 8.56 |
| Ryan N Lichtenwalter | 2 | 504 | 19.64 |
| Nitesh Chawla | 3 | 7257 | 345.79 |