Abstract
There are lots of situations that cannot be described by traditional networks but can be described perfectly by the hypernetwork in the real world. Different from the traditional network, the hypernetwork structure is more complex and poses a great challenge to existing network representation learning methods. Therefore, in order to overcome the challenge of the hypernetwork structure faced by network representation learning, this paper proposes a hypernetwork representation learning method with the set constraint abbreviated as HRSC, which incorporates the hyperedge set associated with the nodes into the process of hypernetwork representation learning to obtain node representation vectors including the hypernetwork topology structure and hyperedge information. Our proposed method is extensively evaluated by the machine learning tasks on four hypernetwork datasets. Experimental results demonstrate that HRSC outperforms other best baseline methods by about 1% on the MovieLens and wordnet datasets in terms of node classification, and outperforms the other best baseline methods, respectively, on average by about 29.03%, 1.94%, 26.27% and 6.24% on the GPS, MovieLens, drug, and wordnet datasets in terms of link prediction.
Highlights
Learning with the Set Constraint.Networks are ubiquitous in our daily life, and many real-life applications focus on mining valuable information from networks
To deal with the above challenge, this paper proposes a universal hypernetwork representation learning method with the set constraint HRSC to effectively incorporate the hyperedges into the process of hypernetwork representation learning, which is formulated as a joint optimization problem solved by the stochastic gradient ascent (SGA) algorithm
In order to deal with these problems, this paper proposes a universal hypernetwork representation learning method with the set constraint to learn discriminative node representation vectors
Summary
Learning with the Set Constraint.Networks are ubiquitous in our daily life, and many real-life applications focus on mining valuable information from networks. A basic issue of data mining is how to learn ideal node representation vectors. To cope with this issue, network representation learning has been proposed to learn a low-dimensional representation vector for each node in the network, which can be applied to lots of machine learning tasks such as node classification [1], link prediction [2], and community detection [3]. The basic assumption of these topology-based representation learning methods is that the nodes with similar topological contexts should be tightly distributed in the low-dimensional vector representation space. Topology-based network representation learning methods cannot effectively capture their similarity. In such a case, other types of heterogeneous information should be incorporated to learn node representation vectors of better quality
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
