LRBM: A Restricted Boltzmann Machine Based Approach for Representation Learning on Linked Data
By Kang Li, Jing Gao, Suxin Guo, Nan Du, Xiaoyi Li, Aidong Zhang. 2014
29 citations recorded by the source corpus.
Linked data consist of both node attributes, e.g., Preferences, posts and degrees, and links which describe the connections between nodes. They have been widely used to represent various network systems, such as social networks, biological networks and etc. Knowledge discovery on linked data is of great importance to many real applications. One of the major challenges of learning linked data is how to effectively and efficiently extract useful information from both node attributes and links in linked data. Current studies on this topic either use selected topological statistics to represent network structures, or linearly map node attributes and network structures to a shared latent feature space. However, while approaches based on statistics may miss critical patterns in network structure, approaches based on linear mappings may not be sufficient to capture the non-linear characteristics of nodes and links. To handle the challenge, we propose, to our knowledge, the first deep learning method to learn from linked data. A restricted Boltzmann machine model named LRBM is developed for representation learning on linked data. In LRBM, we aim to extract the latent feature representation of each node from both node attributes and network structures, non-linearly map each pair of nodes to the links, and use hidden units to control the mapping. The details of how to adapt LRBM for link prediction and node classification on linked data have also been presented. In the experiments, we test the performance of LRBM as well as other baselines on link prediction and node classification. Overall, the extensive experimental evaluations confirm the effectiveness of the proposed LRBM model in mining linked data.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Representation & Self-Supervised Learning, Graph Learning, Signal Processing
How researchers see this paper
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In Artificial Intelligence
- Aditya Grover
- Jure Leskovec
- William L. Hamilton
- Rex Ying
- Marinka Zitnik
- Jiaxuan You
- Yoshua Bengio
- Stefano Ermon
In Computer Vision and Pattern Recognition
- Jure Leskovec
- Aditya Grover
- Jaewon Yang
- Rok Sosic
- Bryan Perozzi
- Santo Fortunato
- Darko Hric
- Samuel A. Assefa
In Representation & Self-Supervised Learning
- Jure Leskovec
- Aditya Grover
- William L. Hamilton
- Stephan Günnemann
- Aleksandar Bojchevski
- Marinka Zitnik
- Philip S. Yu
- Jian Tang
In Graph Learning
- Jure Leskovec
- Aditya Grover
- William L. Hamilton
- Stephan Günnemann
- Aleksandar Bojchevski
- Marinka Zitnik
- Rex Ying
- Jian Tang
In Signal Processing
Citations in the corpus
- node2vec: Scalable Feature Learning for Networks (2016, background): “Finally, for both node and edge based prediction tasks, there is a body of recent work for supervised feature learning based on existing and novel graph-specific deep network architectures [15, 16, 17, 31, 39].”
- LASAGNE: Locality and Structure Aware Graph Node Embedding (2017, unknown): “ss labels [20]. The learning algorithm alternates between prediction of nodes in neighborhood as in DeepWalk and prediction of nodes having the same labels. Further related approaches can be found in [21, 22, 23, 24, 25, 26]. 2.2 Embedding Words with Word2vec Word2vec [27, 28] is a framework for lea”