no code implementations • 21 Feb 2024 • Yuying Zhao, Minghua Xu, Huiyuan Chen, Yuzhong Chen, Yiwei Cai, Rashidul Islam, Yu Wang, Tyler Derr
Recommender systems (RSs) have gained widespread applications across various domains owing to the superior ability to capture users' interests.
no code implementations • 19 Feb 2024 • Yuying Zhao, Yu Wang, Yi Zhang, Pamela Wisniewski, Charu Aggarwal, Tyler Derr
While recommender systems have been designed to improve the user experience in dating platforms by providing personalized recommendations, increasing concerns about fairness have encouraged the development of fairness-aware recommender systems from various perspectives (e. g., gender and race).
no code implementations • 17 Feb 2024 • Yu Wang, Amin Javari, Janani Balaji, Walid Shalaby, Tyler Derr, Xiquan Cui
Then, we adaptively aggregate items' neighbor information considering user intention within the learned session.
no code implementations • 25 Nov 2023 • Ruiqi Feng, Zhichao Hou, Tyler Derr, Xiaorui Liu
The adversarial robustness of Graph Neural Networks (GNNs) has been questioned due to the false sense of security uncovered by strong adaptive attacks despite the existence of numerous defenses.
1 code implementation • 10 Oct 2023 • Anwar Said, Mudassir Shabbir, Tyler Derr, Waseem Abbas, Xenofon Koutsoukos
The superior performance of GNNs often correlates with the availability and quality of node-level features in the input networks.
1 code implementation • 6 Oct 2023 • Yu Wang, Tong Zhao, Yuying Zhao, Yunchao Liu, Xueqi Cheng, Neil Shah, Tyler Derr
Despite the widespread belief that low-degree nodes exhibit poorer LP performance, our empirical findings provide nuances to this viewpoint and prompt us to propose a better metric, Topological Concentration (TC), based on the intersection of the local subgraph of each node with the ones of its neighbors.
1 code implementation • 31 Aug 2023 • Yi Zhang, Yuying Zhao, Zhaoqing Li, Xueqi Cheng, Yu Wang, Olivera Kotevska, Philip S. Yu, Tyler Derr
Despite this progress, there is a lack of a comprehensive overview of the attacks and the techniques for preserving privacy in the graph domain.
no code implementations • 23 Aug 2023 • Anwar Said, Tyler Derr, Mudassir Shabbir, Waseem Abbas, Xenofon Koutsoukos
By laying a solid foundation and fostering continued progress, this survey seeks to inspire researchers to further advance the field of graph unlearning, thereby instilling confidence in the ethical growth of AI systems and reinforcing the responsible application of machine learning techniques in various domains.
1 code implementation • 22 Aug 2023 • Yu Wang, Nedim Lipka, Ryan A. Rossi, Alexa Siu, Ruiyi Zhang, Tyler Derr
Concurrently, the graph traversal agent acts as a local navigator that gathers pertinent context to progressively approach the question and guarantee retrieval quality.
no code implementations • 10 Jul 2023 • Yuying Zhao, Yu Wang, Yunchao Liu, Xueqi Cheng, Charu Aggarwal, Tyler Derr
Additionally, motivated by the concepts of user-level and item-level fairness, we broaden the understanding of diversity to encompass not only the item level but also the user level.
1 code implementation • NeurIPS 2023 • Anwar Said, Roza G. Bayrak, Tyler Derr, Mudassir Shabbir, Daniel Moyer, Catie Chang, Xenofon Koutsoukos
We delve deeply into the dataset generation search space by crafting 35 datasets that encompass static and dynamic brain connectivity, running in excess of 15 baseline methods for benchmarking.
1 code implementation • 7 Dec 2022 • Yuying Zhao, Yu Wang, Tyler Derr
Although research efforts have been devoted to measuring and mitigating bias, they mainly study bias from the result-oriented perspective while neglecting the bias encoded in the decision-making procedure.
1 code implementation • 3 Jul 2022 • Yu Wang, Yuying Zhao, Yi Zhang, Tyler Derr
Graph Neural Networks (GNNs) have been successfully adopted in recommender systems by virtue of the message-passing that implicitly captures collaborative effect.
1 code implementation • 24 Jun 2022 • Yushun Dong, Song Wang, Yu Wang, Tyler Derr, Jundong Li
The low transparency on how the structure of the input network influences the bias in GNN outcome largely limits the safe adoption of GNNs in various decision-critical scenarios.
1 code implementation • 7 Jun 2022 • Yu Wang, Yuying Zhao, Yushun Dong, Huiyuan Chen, Jundong Li, Tyler Derr
Motivated by our analysis, we propose Fair View Graph Neural Network (FairVGNN) to generate fair views of features by automatically identifying and masking sensitive-correlated features considering correlation variation after feature propagation.
1 code implementation • 10 Feb 2022 • Benedek Rozemberczki, Charles Tapley Hoyt, Anna Gogleva, Piotr Grabowski, Klas Karis, Andrej Lamov, Andriy Nikolov, Sebastian Nilsson, Michael Ughetto, Yu Wang, Tyler Derr, Benjamin M Gyori
In this paper, we introduce ChemicalX, a PyTorch-based deep learning library designed for providing a range of state of the art models to solve the drug pair scoring task.
2 code implementations • 1 Dec 2021 • Yu Wang, Yuying Zhao, Neil Shah, Tyler Derr
To this end, we introduce a novel framework, Graph-of-Graph Neural Networks (G$^2$GNN), which alleviates the graph imbalance issue by deriving extra supervision globally from neighboring graphs and locally from stochastic augmentations of graphs.
1 code implementation • 22 Oct 2021 • Yu Wang, Charu Aggarwal, Tyler Derr
Recent years have witnessed the significant success of applying graph neural networks (GNNs) in learning effective node representations for classification.
1 code implementation • 25 Aug 2021 • Yu Wang, Tyler Derr
Nevertheless, iterative propagation restricts the information of higher-layer neighborhoods to be transported through and fused with the lower-layer neighborhoods', which unavoidably results in feature smoothing between neighborhoods in different layers and can thus compromise the performance, especially on heterophily networks.
Ranked #25 on Node Classification on Cora
1 code implementation • 6 Aug 2021 • Xuejiao Tang, Wenbin Zhang, Yi Yu, Kea Turner, Tyler Derr, Mengyu Wang, Eirini Ntoutsi
While image understanding on recognition-level has achieved remarkable advancements, reliable visual scene understanding requires comprehensive image understanding on recognition-level but also cognition-level, which calls for exploiting the multi-source information as well as learning different levels of understanding and extensive commonsense knowledge.
no code implementations • 10 May 2021 • Wei Jin, Xiaorui Liu, Yao Ma, Tyler Derr, Charu Aggarwal, Jiliang Tang
Graph neural networks (GNNs) have received tremendous attention due to their power in learning effective representations for graphs.
1 code implementation • 19 Nov 2020 • Wei Jin, Tyler Derr, Yiqi Wang, Yao Ma, Zitao Liu, Jiliang Tang
Specifically, to balance information from graph structure and node features, we propose a feature similarity preserving aggregation which adaptively integrates graph structure and node features.
1 code implementation • 17 Jun 2020 • Wei Jin, Tyler Derr, Haochen Liu, Yiqi Wang, Suhang Wang, Zitao Liu, Jiliang Tang
Thus, we seek to harness SSL for GNNs to fully exploit the unlabeled data.
no code implementations • 27 May 2020 • Haochen Liu, Zhiwei Wang, Tyler Derr, Jiliang Tang
Recently, neural network based dialogue systems have become ubiquitous in our increasingly digitalized society.
no code implementations • 17 May 2020 • Wenqi Fan, Tyler Derr, Xiangyu Zhao, Yao Ma, Hui Liu, Jian-Ping Wang, Jiliang Tang, Qing Li
In this work, we present our framework CopyAttack, which is a reinforcement learning based black-box attack method that harnesses real users from a source domain by copying their profiles into the target domain with the goal of promoting a subset of items.
1 code implementation • 24 Dec 2019 • Hamid Karimi, Tyler Derr, Jiliang Tang
In this regard, one crucial aspect of deep neural network classifiers that can help us deepen our knowledge about their decision-making behavior is to investigate their decision boundaries.
no code implementations • 13 Sep 2019 • Haochen Liu, Tyler Derr, Zitao Liu, Jiliang Tang
Neural dialogue models have been widely adopted in various chatbot applications because of their good performance in simulating and generalizing human conversations.
no code implementations • 10 Jun 2019 • Yao Ma, Suhang Wang, Tyler Derr, Lingfei Wu, Jiliang Tang
Graph Neural Networks (GNNs) have boosted the performance of many graph related tasks such as node classification and graph classification.
2 code implementations • 30 May 2019 • Wenqi Fan, Tyler Derr, Yao Ma, JianPing Wang, Jiliang Tang, Qing Li
Recent years have witnessed rapid developments on social recommendation techniques for improving the performance of recommender systems due to the growing influence of social networks to our daily life.
no code implementations • 27 Feb 2019 • Tyler Derr, Hamid Karimi, Xiaorui Liu, Jiejun Xu, Jiliang Tang
Network alignment, in general, seeks to discover the hidden underlying correspondence between nodes across two (or more) networks when given their network structure.
2 code implementations • ICDM 2018 • Tyler Derr, Yao Ma, Jiliang Tang
However, since previous GCN models have primarily focused on unsigned networks (or graphs consisting of only positive links), it is unclear how they could be applied to signed networks due to the challenges presented by negative links.
Ranked #2 on Link Sign Prediction on Epinions
Social and Information Networks Physics and Society