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Network representation learning is proposed to make it easier to perform complex inference processes on large-scale networks. It aims to represent each node in the network as a low-dimensional potential representation while preserving the structure and inherent features of the network. Most existing learning methods do not consider the comprehensive structure or the rich semantics of between nodes, which lead to incomplete embeddings. We attempt to find a way to learn network representation while keeping the network structure and relations between nodes to address this issue. In this paper, we propose a Community Aware and Relational Attention (CARA) method to enhance network representation learning. In CARA, community aware random walks capture both the community and local context from a global perspective to enhance the structure embedding. And we design a two-layer attention mechanism to model the semantic relationships between nodes, which can learn different text embedding for different neighbors. Then, these two embeddings are combined together to obtain the node representation. Experiments on four real-world networks show that CARA has better performance than other methods in link prediction and node classification.

Enhanced Network Representation Learning With Community Aware and Relational Attention