Name
Abstract | ||
|---|---|---|
As an effective way of learning node representations in networks, network embedding has attracted increasing research interests recently. Most existing approaches use shallow models and only work on static networks by extracting local or global topology information of each node as the algorithm input. It is challenging for such approaches to learn a desirable node representation on incomplete graphs with a large number of missing links or on dynamic graphs with new nodes joining in. It is even challenging for them to deeply fuse other types of data such as node properties into the learning process to help better represent the nodes with insufficient links. In this paper, we for the first time study the problem of network embedding on incomplete networks. We propose a Multi-View Correlation-learning based Deep Network Embedding method named MVC-DNE to incorporate both the network structure and the node properties for more effectively and efficiently perform network embedding on incomplete networks. Specifically, we consider the topology structure of the network and the node properties as two correlated views. The insight is that the learned representation vector of a node should reflect its characteristics in both views. Under a multi-view correlation learning based deep autoencoder framework, the structure view and property view embeddings are integrated and mutually reinforced through both self-view and cross-view learning. As MVC-DNE can learn a representation mapping function, it can directly generate the representation vectors for the new nodes without retraining the model. Thus it is especially more efficient than previous methods. Empirically, we evaluate MVC-DNE over three real network datasets on two data mining applications, and the results demonstrate that MVC-DNE significantly outperforms state-of-the-art methods.
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Year | DOI | Venue |
|---|---|---|
2017 | 10.1145/3132847.3132975 | CIKM |
Keywords | Field | DocType |
Network Embedding, Incomplete Graph, Deep Learning | Graph,Data mining,Autoencoder,Computer science,Theoretical computer science,Data type,Artificial intelligence,Deep learning,Network embedding,Fuse (electrical),Topological graph theory,Network structure | Conference |
ISBN | Citations | PageRank |
978-1-4503-4918-5 | 12 | 0.49 |
References | Authors | |
19 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Dejian Yang | 1 | 20 | 1.27 |
| Senzhang Wang | 2 | 289 | 28.82 |
| Chaozhuo Li | 3 | 47 | 8.45 |
| Xiaoming Zhang | 4 | 263 | 35.42 |
| Zhoujun Li | 5 | 964 | 115.99 |