Graph Pooling technology plays an important role in graph node classification tasks. Sorting pooling technologies maintain large-value units for pooling graphs of varying sizes. However, by analyzing the statistical characteristic of activated units after pooling, we found that a large number of units dropped by sorting pooling are negative-value units that contain representative information and can contribute considerably to the final decision. To maintain more representative information, we proposed a novel pooling technology, called Geometric Pooling (GP), containing the unique node features with negative values by measuring the similarity of all node features. We reveal the effectiveness of GP from the entropy reduction view. The experiments were conducted on TUdatasets to show the effectiveness of GP. The results showed that the proposed GP outperforms the SOTA graph pooling technologies by $1\%~\sim ~5\%$ with fewer parameters.

Knowledge selection plays a crucial role in knowledge-driven dialogue generation methods, directly influencing the accuracy, relevance, and coherence of generated responses. Existing research often overlooks the handling of disparities between dialogue statements and external knowledge, leading to inappropriate knowledge representation in dialogue generation. To overcome this limitation, this paper proposes an innovative Multi-hop Reasoning Knowledge Selection Module (KMRKSM). Initially, multi-relational graphs containing rich composite operations are encoded to capture graph-aware representations of concepts and relationships. Subsequently, the multi-hop reasoning module dynamically infers along multiple relational paths, aggregating triple evidence to generate knowledge subgraphs closely related to dialogue history. Finally, these generated knowledge subgraphs are combined with dialogue history features and synthesized into comprehensive knowledge features by a decoder. Through automated and manual evaluations, the exceptional performance of KMRKSM in selecting appropriate knowledge is validated. This module efficiently selects knowledge matching the dialogue context through multi-hop reasoning, significantly enhancing the appropriateness of knowledge representation and providing technical support for achieving more natural and human-like dialogue systems.