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. Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. 1609–1618.
  • TreeHash Wei Wu, Mi Jiang, Chuan Luo, Fangfang Li*. (2025). Simple and Efficient Hash Sketching for Tree-Structured Data. Expert Systems with Applications. 267:125973-125984.

• 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. (2024). MPSketch: Message Passing Networks via Randomized Hashing for Efficient Attributed Network Embedding. IEEE Transactions on Cybernetics. 54(5):2941-2954.
  • SketchBANE. Wei Wu, Shiqi Li, Mi Jiang, Chuan Luo and Fangfang Li. (2025). Time- and Space-Efficiently Sketching Billion-Scale Attributed Networks. IEEE Transactions on Knowledge and Data Engineering. 37(2):966-978.
  • VLS2ketch. Wei Wu, Shiqi Li, Ling Chen, Fangfang Li, and Chuan Luo. (2025). Sketching Very Large-scale Dynamic Attributed Networks More Practically. In Proceedings of the ACM on Web Conference 2025 (WWW '25). 5264-5274.