KPGNN

This repository contains the source code and preprocessed dataset for The Web Conference 2021 paper Knowledge-Preserving Incremental Social Event Detection via Heterogeneous GNNs.

To run KPGNN

Step 1) cd /KPGNN

Step 2) run generate_initial_features.py to generate the initial features for the messages (please see Figure 1(b) and Section 3.2 of the paper for more details).

Step 3) run custom_message_graph.py to construct incremental message graphs. To construct small message graphs for test purpose, set test=True when calling construct_incremental_dataset_0922(). To use all the messages (see Table. 4 of the paper for a statistic of the number of messages in the graphs), set test=False.

Step 4) run main.py

Datasets

Twitter dataset

The Twitter dataset [1] is collected to evaluate social event detection methods. After filtering out repeated and irretrievable tweets, the dataset contains 68,841 manually labeled tweets related to 503 event classes, spread over a period of four weeks. Please find the original dataset at http://mir.dcs.gla.ac.uk/resources/

MAVEN dataset

MAVEN [2] is a general domain event detection dataset constructed from Wikipedia documents. We remove sentences (i.e., messages) that are associated with multiple event types. The filtered dataset contains 10,242 messages related to 154 event classes. Please find the original dataset at https://github.com/THU-KEG/MAVEN-dataset

Data format and usage

Please refer to this document for data format and usage.

Baselines

For Word2vec[3], we use the spaCy pre-trained vectors.

For LDA[4], WMD[5], BERT[6], and PP-GCN[7], we use the open-source implementations.

We implement EventX[8] with Python 3.7.3 and BiLSTM[9] with Pytorch 1.6.0. Please refer to the baselines folder.

Citation

If you find this repository helpful, please consider citing the following paper.

@article{cao2021knowledge,
  title={Knowledge-Preserving Incremental Social Event Detection via Heterogeneous GNNs},
  author={Cao, Yuwei and Peng, Hao and Wu, Jia and Dou, Yingtong and Li, Jianxin and Yu, Philip S},
  journal={arXiv preprint arXiv:2101.08747},
  year={2021}
}

[1] Andrew J McMinn, Yashar Moshfeghi, and Joemon M Jose. 2013. Building a large-scale corpus for evaluating event detection on twitter. In Proceedings of the CIKM.ACM, 409–418.

[2] Xiaozhi Wang, Ziqi Wang, Xu Han, Wangyi Jiang, Rong Han, Zhiyuan Liu, Juanzi Li, Peng Li, Yankai Lin, and Jie Zhou. 2020. MAVEN: A Massive General Domain Event Detection Dataset. In Proceedings of EMNLP.

[3] Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. In Proceedings of ICLR.

[4] David M Blei, Andrew Y Ng, and Michael I Jordan. 2003. Latent dirichlet allocation. JMLR 3, Jan (2003), 993–1022.

[5] Matt Kusner, Yu Sun, Nicholas Kolkin, and Kilian Weinberger. 2015. From word embeddings to document distances. In Proceedings of the ICML. 957–966.

[6] Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805 (2018).

[7] Hao Peng, Jianxin Li, Qiran Gong, Yangqiu Song, Yuanxing Ning, Kunfeng Lai, and Philip S. Yu. 2019. Fine-grained event categorization with heterogeneous graph convolutional networks. In Proceedings of the IJCAI. 3238–3245.

[8] Bang Liu, Fred X Han, Di Niu, Linglong Kong, Kunfeng Lai, and Yu Xu. 2020. Story Forest: Extracting Events and Telling Stories from Breaking News. TKDD 14, 3 (2020), 1–28.

[9] Alex Graves and Jürgen Schmidhuber. 2005. Framewise phoneme classification with bidirectional LSTM and other neural network architectures. Neural networks 18, 5-6 (2005), 602–610.