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Graph-Aware Language Model Pre-Training on a Large Graph Corpus Can Help Multiple Graph Applications.

, , , , , , , , , , , and . KDD, page 5270-5281. ACM, (2023)

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On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphs., , , and . CIKM, page 3898-3902. ACM, (2022)Federated Node Classification over Graphs with Latent Link-type Heterogeneity., , and . WWW, page 556-566. ACM, (2023)KoMen: Domain Knowledge Guided Interaction Recommendation for Emerging Scenarios., , , , , , and . WWW, page 1301-1310. ACM, (2022)When to Pre-Train Graph Neural Networks? From Data Generation Perspective!, , , , , , , and . KDD, page 142-153. ACM, (2023)BrainNet: Epileptic Wave Detection from SEEG with Hierarchical Graph Diffusion Learning., , , , , and . KDD, page 2741-2751. ACM, (2022)GraphBLAST: A High-Performance Linear Algebra-based Graph Framework on the GPU., , and . ACM Trans. Math. Softw., 48 (1): 1:1-1:51 (2022)cube2net: Efficient Query-Specific Network Construction with Data Cube Organization., , , , and . CoRR, (2020)Higher-Order Clustering in Heterogeneous Information Networks., , , , and . CoRR, (2018)Heterogeneous Network Representation Learning: Survey, Benchmark, Evaluation, and Beyond., , , , and . CoRR, (2020)Neural Concept Map Generation for Effective Document Classification with Interpretable Structured Summarization., , , , and . SIGIR, page 1629-1632. ACM, (2020)