graph_hashing

A toolbox of randomized hashing algorithms for fast Graph Representation and Network Embedding. We provide two sets of graph hashing algorithms as follows:

• Graph kernels for graph classification

  This problem provides a graph database which consists of multiple graphs, and contains the following steps:

  1. Each graph is represented as the hashcode;
  2. Pairwise hamming similarity calculation between the hashcodes;
  3. Hamming-similarity-based Graph classification.

  We provide the following algorithms:

  • Nested Subtree Hashing (NSH). Bin Li, Xingquan Zhu, Lianhua Chi, Chengqi Zhang. (2012). Nested Subtree Hash Kernels for Large-scale Graph Classification over Streams. Proceedings of the 12th International Conference on Data Mining. 399-408.
  • K-Ary Tree Hashing (KATH). Wei Wu, Bin Li, Ling Chen, Xingquan Zhu, Chengqi Zhang. (2018). K-Ary Tree Hashing for Fast Graph Classification. IEEE Transactions on Knowledge and Data Engineering. 30(5):936-949.
  • SCHash. Xuan Tan, Wei Wu*, Chuan Luo. (2023). SCHash: Speedy Simplicial Complex Neural Networks via Randomized Hashing. to appear in SIGIR 2023.

• Network embedding for node classification, link prediction and node retrieval, etc.

  This task provides a network, and contains the following steps:

  1. Each node is represented as the hashcode;
  2. Pairwise hamming similarity calculation between the hashcodes;
  3. Hamming-similarity-based node classification, link prediction and node retrieval, etc.

  We provide the following algorithms:

  • NetHash. Wei Wu, Bin Li, Ling Chen, Chengqi Zhang. (2018). Efficient Attributed Network Embedding via Recursive Randomized Hashing. Proceedings of the 27th International Joint Conference on Artificial Intelligence. 2861-2867.
  • #GNN. Wei Wu, Bin Li, Chuan Luo and Wolfgang Nejdl. (2021). Hashing-Accelerated Graph Neural Networks for Link Prediction. Proceedings of the 30th Web Conference. 2910-2920.
  • MPSketch. Wei Wu, Bin Li, Chuan Luo, Wolfgang Nejdl and Xuan Tan. (2023). MPSketch: Message Passing Networks via Randomized Hashing for Efficient Attributed Network Embedding. accpted by IEEE Transactions on Cybernetics.