Hypernetwork Representation Learning with the Set Constraint

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.

Hypernetwork Representation Learning with Common Constraints of the Set and Translation

“…HRSC. HRSC [11] incorporates the hyperedge sets associated with the nodes into the process of hypernetwork representation learning.…”

“…Because the computational efficiency of CBOW [2 [22], a topology-derived model [11] based on the nega the network structure was introduced. To be specific this model, the center node n was the positive sam samples, and…”

“…Because the computational efficiency of CBOW [22] is greater than that of skipgram [22], a topology-derived model [11] based on the negative sampling to be used to capture the network structure was introduced. To be specific, in the optimization procedure of this model, the center node n was the positive sample, other nodes were the negative samples, and NEG(n) was the subset of negative samples with a predefined size ds.…”

“…Because the above topology-derived model only considered the network structure, a set constraint model [11] based on the negative sampling to consider both the network structure and the hyperedge was introduced. To be specific, in the optimization procedure of this model, T n was the set of the hyperedges associated with the center node n, and also the set of the nodes associated with the center node n if the hyperedge was regarded as the node.…”

“…As for the hypernetwork, hypernetwork representation learning [11] has been gradually widely studied by researchers. According to the characteristics of hypernetwork representation learning methods, they are divided into expanded spectral analysis and non-expanded methods.…”