Abstract

Node representation learning plays a crucial role in addressing entity-related tasks in a relational network. However, current methods for node representation learning based on graph neural networks often rely on a layer-by-layer perception strategy. This approach compresses node features during propagation, hindering the direct perception of essential information and leading to over-smoothing issues. Moreover, many methods lack a comprehensive consideration of network topology and node semantics, limiting the model's performance. In this paper, we break through the traditional layer-by-layer perception strategy and propose TSCNet, a node representation learning method founded on direct perception strategy and topological and semantic collaborative mining. Initially, TSCNet establishes direct connections between the target node and sampled neighbor nodes at different orders, facilitating the target node directly perceiving neighborhood information. Subsequently, TSCNet introduces static learnable parameters to weigh different-order topologies and utilizes attention mechanisms to weigh the semantic relevance between nodes. Finally, based on the direct connections graph, TSCNet aggregates neighborhood information according to topology and semantic weights, achieving task-adaptive topology and semantic co-mining. We conduct extensive experiments on ten real-world datasets. The model achieves top-ranking results in eight datasets in which the node classification accuracy reaches 41.27% on the Actor dataset and 94.78% on the Texas dataset, significantly outperforming mainstream models. The results demonstrate the effectiveness of the proposed TSCNet.