Yikun Ban, Jingrui He, Hanghang Tong, Zihao Li, Jian Kang, Dongqi Fu, Jiaru Zou, Yunzhe Qi
We lifted 5 functions out of this paper's own repositories and ran 1 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| jiaruzouu/prb | canonical | 1 of 1 |
| jiaruzouu/PRB | canonical | 0 of 4 |
| Function | Status | Where it lives |
|---|---|---|
| construct_adjacency_matrix_from_G | Ran | jiaruzouu/prb/online_link_prediction/PRB_run_amazon_fashion.py code served (permissive licence) · get_code("a279b06e66a0755b") |
| connect_current_nodes_edges | Not yet run | jiaruzouu/PRB/offline_node_classification/PRB_run_citeseer.py code served (permissive licence) · get_code("af3f194eec2ee97b") |
| construct_adjacency_matrix_from_G | Not yet run | jiaruzouu/PRB/offline_node_classification/PRB_run_citeseer.py code served (permissive licence) · get_code("1ab7c72374ace5b3") |
| construct_adjacency_matrix_from_G | Not yet run | jiaruzouu/PRB/online_link_prediction/PRB_run_facebook.py code served (permissive licence) · get_code("1995c82661e2f042") |
| construct_no_edge_graph | Not yet run | jiaruzouu/PRB/offline_node_classification/PRB_run_citeseer.py code served (permissive licence) · get_code("514b5498205cc81c") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Link prediction is a critical problem in graph learning with broad applications such as recommender systems and knowledge graph completion. Numerous research efforts have been directed at solving this problem, including approaches based on similarity metrics and Graph Neural Networks (GNN). However, most existing solutions are still rooted in conventional supervised learning, which makes it challenging to adapt over time to changing customer interests and to address the inherent dilemma of exploitation versus exploration in link prediction. To tackle these challenges, this paper reformulates link prediction as a sequential decision-making process, where each link prediction interaction occurs sequentially. We propose a novel fusion algorithm, PRB (PageRank Bandits), which is the first to combine contextual bandits with PageRank for collaborative exploitation and exploration. We also introduce a new reward formulation and provide a theoretical performance guarantee for PRB. Finally, we extensively evaluate PRB in both online and offline settings, comparing it with bandit-based and graph-based methods. The empirical success of PRB demonstrates the value of the proposed fusion approach. Our code is released at https://github.com/jiaruzouu/PRB * Equal contribution. 38th Conference on Neural Information Processing Systems (NeurIPS 2024).
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2411.01410")
get_code_for_paper("2411.01410")
have("2411.01410")
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