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- research-article
Graph neural networks with adaptive residual
NIPS '21: Proceedings of the 35th International Conference on Neural Information Processing Systems•December 2021, Article No.: 744, pp 9720-9733Graph neural networks (GNNs) have shown the power in graph representation learning for numerous tasks. In this work, we discover an interesting phenomenon that although residual connections in the message passing of GNNs help improve the performance, ...
- research-article
Towards label position bias in graph neural networks
NIPS '23: Proceedings of the 37th International Conference on Neural Information Processing Systems•December 2023, Article No.: 997, pp 22999-23018Graph Neural Networks (GNNs) have emerged as a powerful tool for semi- supervised node classification tasks. However, recent studies have revealed various biases in GNNs stemming from both node features and graph topology. In this work, we uncover a new ...
- research-article
Linear-Time Graph Neural Networks for Scalable Recommendations
WWW '24: Proceedings of the ACM Web Conference 2024•May 2024, pp 3533-3544• https://doi.org/10.1145/3589334.3645486In an era of information explosion, recommender systems are vital tools to deliver personalized recommendations for users. The key of recommender systems is to forecast users' future behaviors based on previous user-item interactions. Due to their strong ...
- abstract
Large-Scale Graph Neural Networks: The Past and New Frontiers
KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining•August 2023, pp 5835-5836• https://doi.org/10.1145/3580305.3599565Graph Neural Networks (GNNs) have gained significant attention in recent years due to their ability to model complex relationships between entities in graph-structured data such as social networks, protein structures, and knowledge graphs. However, due ...
- research-article
How does the Memorization of Neural Networks Impact Adversarial Robust Models?
KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining•August 2023, pp 2801-2812• https://doi.org/10.1145/3580305.3599381Recent studies suggest that "memorization" is one necessary factor for overparameterized deep neural networks (DNNs) to achieve optimal performance. Specifically, the perfectly fitted DNNs can memorize the labels of many atypical samples, generalize ...
- research-article
Enhancing Graph Representations Learning with Decorrelated Propagation
KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining•August 2023, pp 1466-1476• https://doi.org/10.1145/3580305.3599334In recent years, graph neural networks (GNNs) have been widely used in many domains due to their powerful capability in representation learning on graph-structured data. While a majority of extant studies focus on mitigating the over-smoothing problem, ...
- research-article
LazyGNN: large-scale graph neural networks via lazy propagation
ICML'23: Proceedings of the 40th International Conference on Machine Learning•July 2023, Article No.: 1622, pp 38926-38937Recent works have demonstrated the benefits of capturing long-distance dependency in graphs by deeper graph neural networks (GNNs). But deeper GNNs suffer from the long-lasting scalability challenge due to the neighborhood explosion problem in large-...
- research-article
Alternately optimized graph neural networks
ICML'23: Proceedings of the 40th International Conference on Machine Learning•July 2023, Article No.: 502, pp 12411-12429Graph Neural Networks (GNNs) have greatly advanced the semi-supervised node classification task on graphs. The majority of existing GNNs are trained in an end-to-end manner that can be viewed as tackling a bi-level optimization problem. This process is ...
- survey
Trustworthy AI: A Computational Perspective
ACM Transactions on Intelligent Systems and Technology, Volume 14, Issue 1•February 2023, Article No.: 4, pp 1-59 • https://doi.org/10.1145/3546872In the past few decades, artificial intelligence (AI) technology has experienced swift developments, changing everyone’s daily life and profoundly altering the course of human society. The intention behind developing AI was and is to benefit humans by ...
- tutorial
Accepted Tutorials at The Web Conference 2022
- Riccardo Tommasini,
- Senjuti Basu Roy,
- Xuan Wang,
- Hongwei Wang,
- Heng Ji,
- Jiawei Han,
- Preslav Nakov,
- Giovanni Da San Martino,
- Firoj Alam,
- Markus Schedl,
- Elisabeth Lex,
- Akash Bharadwaj,
- Graham Cormode,
- Milan Dojchinovski,
- Jan Forberg,
- Johannes Frey,
- Pieter Bonte,
- Marco Balduini,
- Matteo Belcao,
- Emanuele Della Valle,
- Junliang Yu,
- Hongzhi Yin,
- Tong Chen,
- Haochen Liu,
- Yiqi Wang,
- Wenqi Fan,
- Xiaorui Liu,
- Jamell Dacon,
- Lingjuan Lye,
- Jiliang Tang,
- Aristides Gionis,
- Stefan Neumann,
- Bruno Ordozgoiti,
- Simon Razniewski,
- Hiba Arnaout,
- Shrestha Ghosh,
- Fabian Suchanek,
- Lingfei Wu,
- Yu Chen,
- Yunyao Li,
- Bang Liu,
- Filip Ilievski,
- Daniel Garijo,
- Hans Chalupsky,
- Pedro Szekely,
- Ilias Kanellos,
- Dimitris Sacharidis,
- Thanasis Vergoulis,
- Nurendra Choudhary,
- Nikhil Rao,
- Karthik Subbian,
- Srinivasan Sengamedu,
- Chandan K. Reddy,
- Friedhelm Victor,
- Bernhard Haslhofer,
- George Katsogiannis- Meimarakis,
- Georgia Koutrika,
- Shengmin Jin,
- Danai Koutra,
- Reza Zafarani,
- Yulia Tsvetkov,
- Vidhisha Balachandran,
- Sachin Kumar,
- Xiangyu Zhao,
- Bo Chen,
- Huifeng Guo,
- Yejing Wang,
- Ruiming Tang,
- Yang Zhang,
- Wenjie Wang,
- Peng Wu,
- Fuli Feng,
- Xiangnan He
WWW '22: Companion Proceedings of the Web Conference 2022•April 2022, pp 391-399• https://doi.org/10.1145/3487553.3547182This paper summarizes the content of the 20 tutorials that have been given at The Web Conference 2022: 85% of these tutorials are lecture style, and 15% of these are hands on.
- research-article
Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective
KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining•August 2022, pp 709-719• https://doi.org/10.1145/3534678.3539445Recent years have witnessed remarkable success achieved by graph neural networks (GNNs) in many real-world applications such as recommendation and drug discovery. Despite the success, oversmoothing has been identified as one of the key issues which limit ...
- research-article
Graph Trend Filtering Networks for Recommendation
SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval•July 2022, pp 112-121• https://doi.org/10.1145/3477495.3531985Recommender systems aim to provide personalized services to users and are playing an increasingly important role in our daily lives. The key of recommender systems is to predict how likely users will interact with items based on their historical online ...
- Doctoral Theses
Efficient and Secure Message Passing for Machine Learning
AbstractMachine learning (ML) techniques have brought revolutionary impact to human society, and they will continue to act as technological innovators in the future. To broaden its impact, it is urgent to solve the emerging and critical challenges in ...
- research-article
Graph Feature Gating Networks
CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management•October 2021, pp 813-822• https://doi.org/10.1145/3459637.3482434Graph neural networks (GNNs) have received tremendous attention due to their power in learning effective representations for graphs. Most GNNs follow a message-passing scheme where the node representations are updated by aggregating and transforming the ...
- research-article
Deep Adversarial Network Alignment
CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management•October 2021, pp 352-361• https://doi.org/10.1145/3459637.3482418Network alignment, in general, seeks to discover the hidden underlying correspondence between nodes across two (or more) networks when given their network structure. However, most existing network alignment methods have added assumptions of additional ...
- research-article
A Unified View on Graph Neural Networks as Graph Signal Denoising
CIKM '21: Proceedings of the 30th ACM International Conference on Information & Knowledge Management•October 2021, pp 1202-1211• https://doi.org/10.1145/3459637.3482225Graph Neural Networks (GNNs) have risen to prominence in learning representations for graph structured data. A single GNN layer typically consists of a feature transformation and a feature aggregation operation. The former normally uses feed-forward ...
- abstract
Graph Representation Learning: Foundations, Methods, Applications and Systems
- Wei Jin,
- Yao Ma,
- Yiqi Wang,
- Xiaorui Liu,
- Jiliang Tang,
- Yukuo Cen,
- Jiezhong Qiu,
- Jie Tang,
- Chuan Shi,
- Yanfang Ye,
- Jiawei Zhang,
- Philip S. Yu
KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining•August 2021, pp 4044-4045• https://doi.org/10.1145/3447548.3470824Graphs such as social networks and molecular graphs are ubiquitous data structures in the real world. Due to their prevalence, it is of great research importance to extract meaningful patterns from graph structured data so that downstream tasks can be ...
- abstract
Adversarial Robustness in Deep Learning: From Practices to Theories
KDD '21: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining•August 2021, pp 4086-4087• https://doi.org/10.1145/3447548.3470812Deep neural networks (DNNs) have achieved unprecedented accomplishments in various machine learning tasks. However, recent studies demonstrate that DNNs are extremely vulnerable to adversarial examples. They are manually synthesized input samples which ...
- research-article
Graph Structure Learning for Robust Graph Neural Networks
KDD '20: Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining•August 2020, pp 66-74• https://doi.org/10.1145/3394486.3403049Graph Neural Networks (GNNs) are powerful tools in representation learning for graphs. However, recent studies show that GNNs are vulnerable to carefully-crafted perturbations, called adversarial attacks. Adversarial attacks can easily fool GNNs in ...
- research-article
Epidemic Graph Convolutional Network
WSDM '20: Proceedings of the 13th International Conference on Web Search and Data Mining•January 2020, pp 160-168• https://doi.org/10.1145/3336191.3371807A growing trend recently is to harness the structure of today's big data, where much of the data can be represented as graphs. Simultaneously, graph convolutional networks (GCNs) have been proposed and since seen rapid development. More recently, due to ...
