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Query-Specific Deep Embedding of Content-Rich Network. | LitMetric

AI Article Synopsis

  • The paper presents a method for similarity searching in a content-rich network that incorporates both node and edge information along with the content of nodes.
  • It employs convolutional neural networks (CNN) to represent node content and graph convolutional networks (GCN) to merge these representations with neighboring nodes, ultimately transforming them into Gaussian distributions for similarity measurement.
  • An iterative algorithm minimizes losses related to node identification, structure preservation, and relevance, and experiments demonstrate the method's effectiveness over existing techniques, particularly in innovation networks.

Article Abstract

In this paper, we propose to embed a content-rich network for the purpose of similarity searching for a query node. In this network, besides the information of the nodes and edges, we also have the content of each node. We use the convolutional neural network (CNN) to represent the content of each node and then use the graph convolutional network (GCN) to further represent the node by merging the representations of its neighboring nodes. The GCN output is further fed to a deep encoder-decoder model to convert each node to a Gaussian distribution and then convert back to its node identity. The dissimilarity between the two nodes is measured by the Wasserstein distance between their Gaussian distributions. We define the nodes of the network to be positives if they are relevant to the query node and negative if they are irrelevant. The labeling of the positives/negatives is based on an upper bound and a lower bound of the Wasserstein distances between the candidate nodes and the query nodes. We learn the parameters of CNN, GCN, encoder-decoder model, Gaussian distributions, and the upper bound and lower bounds jointly. The learning problem is modeled as a minimization problem to minimize the losses of node identification, network structure preservation, positive/negative query-specific relevance-guild distance, and model complexity. An iterative algorithm is developed to solve the minimization problem. We conducted experiments over benchmark networks, especially innovation networks, to verify the effectiveness of the proposed method and showed its advantage over the state-of-the-art methods.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7468613PMC
http://dx.doi.org/10.1155/2020/5943798DOI Listing

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