An Efficient Automatic Meta-Path Selection for Social Event Detection via Hyperbolic Space
Authors: 
Zitai Qiu, Congbo Ma, Jia Wu, 
Jian YangAuthors Info & Claims
Zitai Qiu, Congbo Ma, Jia Wu, Jian YangAuthors Info & ClaimsPages 2519 - 2529
Abstract
Social events reflect changes in communities, such as natural disasters and emergencies. Detection of these situations can help residents and organizations in the community avoid danger and reduce losses. The complex nature of social messages makes social event detection on social media challenging. The challenges that have a greater impact on social media detection models are as follows: (1) the amount of social media data is huge but its availability is small; (2) social media data is a tree structure and traditional Euclidean space embedding will distort embedded features; and (3) the heterogeneity of social media networks makes existing models unable to capture rich information well. To solve the above challenges, we propose a Heterogeneous Information Graph representation via Hyperbolic space combined with an Automatic Meta-path selection (GraphHAM) model, an efficient framework that automatically selects the meta-path's weight and combines hyperbolic space to learn information on social media. In particular, we apply an efficient automatic meta-path selection technique and convert the selected meta-path into a vector, thereby reducing the requisite amount of labeled data for the model. We also design a novel Hyperbolic Multi-Layer Perceptron (HMLP) to further learn the semantic and structural information of social information. Extensive experiments show that GraphHAM can achieve outstanding performance on real-world data using only 20% of the whole dataset as the training set. Our code can be found on GitHub https://github.com/ZITAIQIU/GraphHAM.
Supplemental Material
MP4 File
Supplemental video
- Download
- 37.63 MB
References
[1]
Aaron B Adcock, Blair D Sullivan, and Michael W Mahoney. 2013. Tree-like structure in large social and information networks. In 2013 IEEE 13th international conference on data mining. IEEE, Dallas, TX, USA, 1--10.
[2]
Imad Afyouni, Zaher Al Aghbari, and Reshma Abdul Razack. 2022. Multi-feature, multi-modal, and multi-source social event detection: A comprehensive survey. Information Fusion 79 (2022), 279--308.
[3]
Alaa Alharbi and Mark Lee. 2021. Kawarith: an Arabic Twitter corpus for crisis events. In Proceedings of the Sixth Arabic Natural Language Processing Workshop. Association for Computational Linguistics, Kyiv, Ukraine (Virtual), 42--52.
[4]
Joeran Beel, Bela Gipp, Stefan Langer, and Corinna Breitinger. 2016. Paper recommender systems: a literature survey. International Journal on Digital Libraries 17 (2016), 305--338.
[5]
DavidMBlei, AndrewY Ng, and Michael I Jordan. 2003. Latent dirichlet allocation. Journal of machine Learning research 3, Jan (2003), 993--1022.
[6]
Yuwei Cao, Hao Peng, Jia Wu, Yingtong Dou, Jianxin Li, and Philip S Yu. 2021. Knowledge-preserving incremental social event detection via heterogeneous gnns. In Proceedings of the Web Conference 2021. ACM, Ljubljana, Slovenia, 3383--3395.
[7]
Yuwei Cao, Hao Peng, Zhengtao Yu, and Philip S Yu. 2023. Hierarchical and Incremental Structural Entropy Minimization for Unsupervised Social Event Detection. CoRR abs/2312.11891 (2023), 13 pages. arXiv:2312.11891
[8]
Ines Chami, Zhitao Ying, Christopher Ré, and Jure Leskovec. 2019. Hyperbolic graph convolutional neural networks. In Advances in neural information processing systems, Vol. 32. NeurIPS, Vancouver, BC, Canada, 4869--4880.
[9]
Wanqiu Cui, Junping Du, Dawei Wang, Feifei Kou, and Zhe Xue. 2021. MVGAN: Multi-view graph attention network for social event detection. ACM Transactions on Intelligent Systems and Technology (TIST) 12, 3 (2021), 1--24.
[10]
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. Association for Computational Linguistics, Minneapolis, MN, USA, 4171--4186.
[11]
Mateusz Fedoryszak, Brent Frederick, Vijay Rajaram, and Changtao Zhong. 2019. Real-time event detection on social data streams. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining. ACM, Anchorage, AK, USA, 2774--2782.
[12]
Xingcheng Fu, Jianxin Li, JiaWu, Qingyun Sun, Cheng Ji, SenzhangWang, Jiajun Tan, Hao Peng, and S Yu Philip. 2021. ACE-HGNN: adaptive curvature exploration hyperbolic graph neural network. In 2021 IEEE international conference on data mining (ICDM). IEEE, Auckland, New Zealand, 111--120.
[13]
Xinyu Fu, Jiani Zhang, Ziqiao Meng, and Irwin King. 2020. Magnn: Metapath aggregated graph neural network for heterogeneous graph embedding. In Proceedings of The Web Conference 2020. ACM, Taipei, Taiwan, 2331--2341.
[14]
Octavian Ganea, Gary Bécigneul, and Thomas Hofmann. 2018. Hyperbolic entailment cones for learning hierarchical embeddings. In International Conference on Machine Learning. PMLR, Stockholm, Sweden, 1646--1655.
[15]
Yuanyuan Guo, Zehua Zang, Hang Gao, Xiao Xu, Rui Wang, Lixiang Liu, and Jiangmeng Li. 2024. Unsupervised social event detection via hybrid graph contrastive learning and reinforced incremental clustering. Knowledge-Based Systems 284 (2024), 16 pages.
[16]
Birger Iversen. 1992. Hyperbolic geometry. Cambridge University Press.
[17]
Armand Joulin, Edouard Grave, Piotr Bojanowski, Matthijs Douze, Hérve Jégou, and Tomas Mikolov. 2016. FastText.zip: Compressing text classification models. CoRR abs/1612.03651 (2016), 13 pages.
[18]
Chenliang Li, Haoran Wang, Zhiqian Zhang, Aixin Sun, and Zongyang Ma. 2016. Topic modeling for short texts with auxiliary word embeddings. In Proceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval. ACM, Pisa, Italy, 165--174.
[19]
Qimai Li, Zhichao Han, and Xiao-Ming Wu. 2018. Deeper insights into graph convolutional networks for semi-supervised learning. In Proceedings of the AAAI conference on artificial intelligence. AAAI, New Orleans, Louisiana, USA, 3538--3545.
[20]
Qian Li, Hao Peng, Jianxin Li, Jia Wu, Yuanxing Ning, Lihong Wang, S Yu Philip, and Zheng Wang. 2021. Reinforcement learning-based dialogue guided event extraction to exploit argument relations. IEEE/ACM Transactions on Audio, Speech, and Language Processing 30 (2021), 520--533.
[21]
Yi Li, Yilun Jin, Guojie Song, Zihao Zhu, Chuan Shi, and Yiming Wang. 2021. GraphMSE: efficient meta-path selection in semantically aligned feature space for graph neural networks. In Proceedings of the AAAI Conference on Artificial Intelligence. AAAI, Virtual Event, 4206--4214.
[22]
Xiaoxiao Ma, JiaWu, Shan Xue, Jian Yang, Chuan Zhou, Quan Z Sheng, Hui Xiong, and Leman Akoglu. 2021. A comprehensive survey on graph anomaly detection with deep learning. IEEE Transactions on Knowledge and Data Engineering 35, 12 (2021), 12012--12038.
[23]
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 22nd ACM international conference on Information & Knowledge Management. ACM, San Francisco, CA, USA, 409--418.
[24]
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. In 1st International Conference on Learning Representations. ICLR, Scottsdale, Arizona, USA, 12 pages.
[25]
Alexandra Olteanu, Carlos Castillo, Fernando Diaz, and Sarah Vieweg. 2014. Crisislex: A lexicon for collecting and filtering microblogged communications in crises. In Proceedings of the international AAAI conference on web and social media. AAAI, Ann Arbor, Michigan, USA, 376--385.
[26]
Viktor Pekar, Jane Binner, Hossein Najafi, Chris Hale, and Vincent Schmidt. 2020. Early detection of heterogeneous disaster events using social media. Journal of the Association for Information Science and Technology 71, 1 (2020), 43--54.
[27]
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 Twenty-Eighth International Joint Conference on Artificial Intelligence. IJCAI, Macao, China, 3238--3245.
[28]
Hao Peng, Ruitong Zhang, Shaoning Li, Yuwei Cao, Shirui Pan, and S Yu Philip. 2022. Reinforced, incremental and cross-lingual event detection from social messages. IEEE Transactions on Pattern Analysis and Machine Intelligence 45, 1 (2022), 980--998.
[29]
Hao Peng, Ruitong Zhang, Shaoning Li, Yuwei Cao, Shirui Pan, and S Yu Philip. 2022. Reinforced, incremental and cross-lingual event detection from social messages. IEEE Transactions on Pattern Analysis and Machine Intelligence 45, 1 (2022), 980--998.
[30]
Wei Peng, Tuomas Varanka, Abdelrahman Mostafa, Henglin Shi, and Guoying Zhao. 2021. Hyperbolic deep neural networks: A survey. IEEE Transactions on pattern analysis and machine intelligence 44, 12 (2021), 10023--10044.
[31]
Jeffrey Pennington, Richard Socher, and Christopher D Manning. 2014. Glove: Global vectors for word representation. In Proceedings of the 2014 conference on empirical methods in natural language processing (EMNLP). ACL, Doha, Qatar, 1532--1543.
[32]
Shengsheng Qian, Hong Chen, Dizhan Xue, Quan Fang, and Changsheng Xu. 2023. Open-World Social Event Classification. In Proceedings of the ACM Web Conference 2023. ACM, Austin, TX, USA, 1562--1571.
[33]
Zitai Qiu, Jia Wu, Jian Yang, Xing Su, and Charu C Aggarwal. 2023. Heterogeneous Social Event Detection via Hyperbolic Graph Representations. CoRR abs/2302.10362 (2023), 14 pages. arXiv:2302.10362
[34]
Juan Ramos. 2003. Using tf-idf to determine word relevance in document queries. In Proceedings of the first instructional conference on machine learning. Citeseer, 29--48.
[35]
Tian Shi, Kyeongpil Kang, Jaegul Choo, and Chandan K Reddy. 2018. Short-text topic modeling via non-negative matrix factorization enriched with local wordcontext correlations. In Proceedings of the 2018 World Wide Web Conference. ACM, Lyon, France, 1105--1114.
[36]
Yizhou Sun, Jiawei Han, Xifeng Yan, Philip S Yu, and Tianyi Wu. 2011. Pathsim: Meta path-based top-k similarity search in heterogeneous information networks. Proceedings of the VLDB Endowment 4, 11 (2011), 992--1003.
[37]
Petar Velickovic, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Lio, and Yoshua Bengio. 2018. Graph attention networks. In 6th International Conference on Learning Representations. ICLR, Vancouver, BC, Canada, 12 pages.
[38]
Chenguang Wang, Yangqiu Song, Haoran Li, Ming Zhang, and Jiawei Han. 2018. Unsupervised meta-path selection for text similarity measure based on heterogeneous information networks. Data Mining and Knowledge Discovery 32 (2018), 1735--1767.
[39]
Hu Wang, Guansong Pang, Chunhua Shen, and Congbo Ma. 2019. Unsupervised representation learning by predicting random distances. In Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence. IJCAI, Yokohama, Japan, 2950--2956.
[40]
XiaoWang, Houye Ji, Chuan Shi, BaiWang, Yanfang Ye, Peng Cui, and Philip S Yu. 2019. Heterogeneous graph attention network. In The world wide web conference. ACM, San Francisco, CA, USA, 2022--2032.
[41]
Xiaokai Wei, Zhiwei Liu, Lichao Sun, and Philip S Yu. 2018. Unsupervised metapath reduction on heterogeneous information networks. CoRR abs/1810.12503 (2018), 8 pages. arXiv:arXiv:1810.12503
[42]
Haoran Yang, Hongxu Chen, Shirui Pan, Lin Li, Philip S Yu, and Guandong Xu. 2022. Dual space graph contrastive learning. In Proceedings of the ACM Web Conference 2022. ACM, Lyon, France, 1238--1247.
[43]
Seongjun Yun, Minbyul Jeong, Raehyun Kim, Jaewoo Kang, and Hyunwoo J Kim. 2019. Graph transformer networks. In Advances in Neural Information Processing Systems. NeurIPS, Vancouver, BC, Canada, 11960--11970.
[44]
Chuxu Zhang, Dongjin Song, Chao Huang, Ananthram Swami, and Nitesh V Chawla. 2019. Heterogeneous graph neural network. In Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery & data mining. ACM, Anchorage, AK, USA, 793--803.
[45]
Han Zhou, Hongpeng Yin, Hengyi Zheng, and Yanxia Li. 2020. A survey on multi-modal social event detection. Knowledge-Based Systems 195 (2020), 105695.
Index Terms
- An Efficient Automatic Meta-Path Selection for Social Event Detection via Hyperbolic Space
Recommendations
Knowledge-Preserving Incremental Social Event Detection via Heterogeneous GNNs
WWW '21: Proceedings of the Web Conference 2021Social events provide valuable insights into group social behaviors and public concerns and therefore have many applications in fields such as product recommendation and crisis management. The complexity and streaming nature of social messages make it ...
Streaming Social Event Detection and Evolution Discovery in Heterogeneous Information Networks
Events are happening in real world and real time, which can be planned and organized for occasions, such as social gatherings, festival celebrations, influential meetings, or sports activities. Social media platforms generate a lot of real-time text ...
What Just Happened? A Framework for Social Event Detection and Contextualisation
HICSS '15: Proceedings of the 2015 48th Hawaii International Conference on System SciencesIn course of a breaking news event, such as natural calamity, political uproar etc., a massive crowd sourced data is generated over social media which makes social media platforms an important source of information in such scenarios. The value of the ...
Comments
Information & Contributors
Information
Published In
May 2024
4826 pages
ISBN:9798400701719
DOI:10.1145/3589334
- General Chairs:
- Tat-Seng Chua,
- Chong-Wah Ngo,
- Proceedings Chair:
- Roy Ka-Wei Lee,
- Program Chairs:
- Ravi Kumar,
- Hady W. Lauw
Copyright © 2024 ACM.
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].
Sponsors
Publisher
Association for Computing Machinery
New York, NY, United States
Publication History
Published: 13 May 2024
Check for updates
Author Tags
Qualifiers
- Research-article
Funding Sources
Conference
WWW '24
Sponsor:
Acceptance Rates
Overall Acceptance Rate 1,899 of 8,196 submissions, 23%
Contributors
Other Metrics
Bibliometrics & Citations
Bibliometrics
Article Metrics
- 0Total Citations
- 499Total Downloads
- Downloads (Last 12 months)499
- Downloads (Last 6 weeks)70
Reflects downloads up to 28 Feb 2025
Other Metrics
Citations
View Options
Login options
Check if you have access through your login credentials or your institution to get full access on this article.
Sign in