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Found 7 papers, 7 papers with code
One for All: Towards Training One Graph Model for All Classification Tasks
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.
Graph Contrastive Learning Meets Graph Meta Learning: A Unified Method for Few-shot Node Tasks
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.
Distance-Restricted Folklore Weisfeiler-Leman GNNs with Provable Cycle Counting Power
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.
Extending the Design Space of Graph Neural Networks by Rethinking Folklore Weisfeiler-Lehman
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
A Visual Active Search Framework for Geospatial Exploration
Many problems can be viewed as forms of geospatial search aided by aerial imagery, with examples ranging from detecting poaching activity to human trafficking.
Reward Delay Attacks on Deep Reinforcement Learning
We find that this mitigation remains insufficient to ensure robustness to attacks that delay, but preserve the order, of rewards.
How Powerful are K-hop Message Passing Graph Neural Networks
Recently, researchers extended 1-hop message passing to K-hop message passing by aggregating information from K-hop neighbors of nodes simultaneously.
