A large number of studies have emerged for Multimodal Knowledge Graph Completion (MKGC) to predict the missing links in MKGs. However, fewer studies have been proposed to study the inductive MKGC (IMKGC) involving emerging entities unseen during training. Existing inductive approaches focus on learning textual entity representations, which neglect rich semantic information in visual modality. Moreover, they focus on aggregating structural neighbors from existing KGs, which of emerging entities are usually limited. However, the semantic neighbors are decoupled from the topology linkage and usually imply the true target entity. In this paper, we propose the IMKGC task and a semantic neighbor retrieval-enhanced IMKGC framework CMR, where the contrast brings the helpful semantic neighbors close, and then the memorize supports semantic neighbor retrieval to enhance inference. Specifically, we first propose a unified cross-modal contrastive learning to simultaneously capture the textual-visual and textual-textual correlations of query-entity pairs in a unified representation space. The contrastive learning increases the similarity of positive query-entity pairs, therefore making the representations of helpful semantic neighbors close. Then, we explicitly memorize the knowledge representations to support the semantic neighbor retrieval. At test time, we retrieve the nearest semantic neighbors and interpolate them to the query-entity similarity distribution to augment the final prediction. Extensive experiments validate the effectiveness of CMR on three inductive MKGC datasets. Codes are available at https://github.com/OreOZhao/CMR.

User mobility behaviors frequently exhibit a spatial clustering phenomenon, wherein points of interest (POIs) visited by the same user tend to be in close proximity. Consequently, leveraging geographical influences for user preference modeling remains a prevalent approach in POI recommendation tasks. However, existing studies often overlook users’ hidden geographical habits for the following reasons: (1) Geographical features are commonly approximated by manually partitioned regions or fixed distributions, inadequately capturing the nuanced spatial proximity among POIs. (2) POIs with high geographical correlations are not explicitly incorporated as feedback signals during the training process, resulting in a lack of spatial clustering pattern learning within users’ preference representations. This paper introduces GeoCo, aGeographicalCorrelation enhanced network for POI recommendation. First, we model POIs’ geographical features using fine-grained hierarchical sequences to capture multilevel spatial relations. Subsequently, we propose a pre-training network that employs the sentence similarity assessment technique to comprehend the semantics of geographical correlations. Second, we introduce a novel multi-objective training process that intuitively learns spatial clustering patterns through user mobility behaviors. Extensive experiments conducted on two location-based social network (LBSN) datasets, Gowalla and Foursquare, demonstrate the superiority of our proposed model over fourteen state-of-the-art baseline models in POI recommendation tasks. Compared with the baselines, GeoCo has achieved a performance improvement of at least 5$\%$in Rec@5 and HR@5 on both datasets. Furthermore, we verify the effectiveness of pre-trained location vectors and the multi-objective training process in enhancing the model's understanding of geographical correlations for user preference construction.

Identification of drug-target interactions (DTIs) is crucial for drug discovery and drug repositioning. Existing graph neural network (GNN) based methods only aggregate information from directly connected nodes restricted in a drug-related or a target-related network, and are incapable of capturing long-range dependencies in the biological heterogeneous graph. In this paper, we propose the heterogeneous graph attention network (HGAN) to capture the complex structures and rich semantics in the biological heterogeneous graph for DTI prediction. HGAN enhances heterogeneous graph structure learning from both the intra-layer perspective and the inter-layer perspective. Concretely, we develop an enhanced graph attention diffusion layer (EGADL), which efficiently builds connections between node pairs that may not be directly connected, enabling information passing from important nodes multiple hops away. By stacking multiple EGADLs, we further enlarge the receptive field from the inter-layer perspective. HGAN advances 15 state-of-the-art methods on two heterogeneous biological datasets, achieving the results near to 1 in terms of AUC and AUPR. We also find that enlarging receptive fields from the inter-layer perspective (stacking layers) is more effective than that from the intra-layer perspective (attention diffusion) for HGAN to achieve promising DTI prediction performances. The code is available at https://github.com/Zora-LM/HGAN-DTI.