1 code implementation • 29 Sep 2023 • Hao liu, Jiarui Feng, Lecheng Kong, Ningyue Liang, DaCheng Tao, Yixin Chen, Muhan Zhang
For in-context learning on graphs, OFA introduces a novel graph prompting paradigm that appends prompting substructures to the input graph, which enables it to address varied tasks without fine-tuning.
1 code implementation • 19 Sep 2023 • Hao liu, Jiarui Feng, Lecheng Kong, DaCheng Tao, Yixin Chen, Muhan Zhang
In our study, we first identify two crucial advantages of contrastive learning compared to meta learning, including (1) the comprehensive utilization of graph nodes and (2) the power of graph augmentations.
1 code implementation • NeurIPS 2023 • Junru Zhou, Jiarui Feng, Xiyuan Wang, Muhan Zhang
Many of the proposed GNN models with provable cycle counting power are based on subgraph GNNs, i. e., extracting a bag of subgraphs from the input graph, generating representations for each subgraph, and using them to augment the representation of the input graph.
1 code implementation • NeurIPS 2023 • Jiarui Feng, Lecheng Kong, Hao liu, DaCheng Tao, Fuhai Li, Muhan Zhang, Yixin Chen
We theoretically prove that even if we fix the space complexity to $O(n^k)$ (for any $k\geq 2$) in $(k, t)$-FWL, we can construct an expressiveness hierarchy up to solving the graph isomorphism problem.
Ranked #2 on Graph Regression on ZINC-500k
1 code implementation • 28 Nov 2022 • Anindya Sarkar, Michael Lanier, Scott Alfeld, Jiarui Feng, Roman Garnett, Nathan Jacobs, Yevgeniy Vorobeychik
Many problems can be viewed as forms of geospatial search aided by aerial imagery, with examples ranging from detecting poaching activity to human trafficking.
1 code implementation • 8 Sep 2022 • Anindya Sarkar, Jiarui Feng, Yevgeniy Vorobeychik, Christopher Gill, Ning Zhang
We find that this mitigation remains insufficient to ensure robustness to attacks that delay, but preserve the order, of rewards.
1 code implementation • 26 May 2022 • Jiarui Feng, Yixin Chen, Fuhai Li, Anindya Sarkar, Muhan Zhang
Recently, researchers extended 1-hop message passing to K-hop message passing by aggregating information from K-hop neighbors of nodes simultaneously.