Name
Abstract | ||
|---|---|---|
Mining outliers in heterogeneous networks is crucial to many applications, but challenges abound. In this paper, we focus on identifying meta-path-based outliers in heterogeneous information network (HIN), and calculate the similarity between different types of objects. We propose a meta-path-based outlier detection method (MPOutliers) in heterogeneous information network to deal with problems in one go under a unified framework. MPOutliers calculates the heterogeneous reachable probability by combining different types of objects and their relationships. It discovers the semantic information among nodes in heterogeneous networks, instead of only considering the network structure. It also computes the closeness degree between nodes with the same type, which extends the whole heterogeneous network. Moreover, each node is assigned with a reliable weighting to measure its authority degree. Substantial experiments on two real datasets (AMiner and Movies dataset) show that our proposed method is very effective and efficient for outlier detection. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1007/s11704-018-7289-4 | Frontiers of Computer Science |
Keywords | Field | DocType |
data mining, heterogeneous information network, outlier detection, short text similarity | Anomaly detection,Weighting,Closeness,Computer science,Outlier,Semantic information,Artificial intelligence,Heterogeneous network,Machine learning,Network structure | Journal |
Volume | Issue | ISSN |
14 | 2 | 2095-2236 |
Citations | PageRank | References |
1 | 0.35 | 0 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Lu Liu | 1 | 28 | 4.39 |
| Shang Wang | 2 | 1 | 0.35 |