no code implementations • EMNLP 2020 • Yu Wang, Yun Li, Hanghang Tong, Ziye Zhu
Specifically, we design (1) Head-Tail Detector based on the multi-head self-attention mechanism and bi-affine classifier to detect boundary tokens, and (2) Token Interaction Tagger based on traditional sequence labeling approaches to characterize the internal token connection within the boundary.
no code implementations • Findings (EMNLP) 2021 • Zixuan Zhang, Hongwei Wang, Han Zhao, Hanghang Tong, Heng Ji
Relations in most of the traditional knowledge graphs (KGs) only reflect static and factual connections, but fail to represent the dynamic activities and state changes about entities.
1 code implementation • NAACL (TextGraphs) 2021 • Qi Zeng, Manling Li, Tuan Lai, Heng Ji, Mohit Bansal, Hanghang Tong
Current methods for event representation ignore related events in a corpus-level global context.
1 code implementation • 30 Dec 2024 • Lecheng Zheng, Baoyu Jing, Zihao Li, Zhichen Zeng, Tianxin Wei, Mengting Ai, Xinrui He, Lihui Liu, Dongqi Fu, Jiaxuan You, Hanghang Tong, Jingrui He
Graph Self-Supervised Learning (SSL) has emerged as a pivotal area of research in recent years.
no code implementations • 21 Dec 2024 • Yuchen Yan, Yuzhong Chen, Huiyuan Chen, Xiaoting Li, Zhe Xu, Zhichen Zeng, Lihui Liu, Zhining Liu, Hanghang Tong
Furthermore, we highlight that the edge heterophily issue and the temporal heterophily issue often co-exist in event-based continuous graphs, giving rise to the temporal edge heterophily challenge.
no code implementations • 30 Nov 2024 • Lihui Liu, ZiHao Wang, Hanghang Tong
Knowledge graph reasoning is pivotal in various domains such as data mining, artificial intelligence, the Web, and social sciences.
no code implementations • 20 Nov 2024 • Deming Chen, Alaa Youssef, Ruchi Pendse, André Schleife, Bryan K. Clark, Hendrik Hamann, Jingrui He, Teodoro Laino, Lav Varshney, YuXiong Wang, Avirup Sil, Reyhaneh Jabbarvand, Tianyin Xu, Volodymyr Kindratenko, Carlos Costa, Sarita Adve, Charith Mendis, Minjia Zhang, Santiago Núñez-Corrales, Raghu Ganti, Mudhakar Srivatsa, Nam Sung Kim, Josep Torrellas, Jian Huang, Seetharami Seelam, Klara Nahrstedt, Tarek Abdelzaher, Tamar Eilam, Huimin Zhao, Matteo Manica, Ravishankar Iyer, Martin Hirzel, Vikram Adve, Darko Marinov, Hubertus Franke, Hanghang Tong, Elizabeth Ainsworth, Han Zhao, Deepak Vasisht, Minh Do, Fabio Oliveira, Giovanni Pacifici, Ruchir Puri, Priya Nagpurkar
This white paper, developed through close collaboration between IBM Research and UIUC researchers within the IIDAI Institute, envisions transforming hybrid cloud systems to meet the growing complexity of AI workloads through innovative, full-stack co-design approaches, emphasizing usability, manageability, affordability, adaptability, efficiency, and scalability.
no code implementations • 20 Nov 2024 • Xiaolong Liu, Zhichen Zeng, Xiaoyi Liu, Siyang Yuan, Weinan Song, Mengyue Hang, Yiqun Liu, Chaofei Yang, Donghyun Kim, Wen-Yen Chen, Jiyan Yang, Yiping Han, Rong Jin, Bo Long, Hanghang Tong, Philip S. Yu
Recent advances in foundation models have established scaling laws that enable the development of larger models to achieve enhanced performance, motivating extensive research into large-scale recommendation models.
no code implementations • 15 Nov 2024 • Zhichen Zeng, Xiaolong Liu, Mengyue Hang, Xiaoyi Liu, Qinghai Zhou, Chaofei Yang, Yiqun Liu, Yichen Ruan, Laming Chen, Yuxin Chen, Yujia Hao, Jiaqi Xu, Jade Nie, Xi Liu, Buyun Zhang, Wei Wen, Siyang Yuan, Kai Wang, Wen-Yen Chen, Yiping Han, Huayu Li, Chunzhi Yang, Bo Long, Philip S. Yu, Hanghang Tong, Jiyan Yang
A mutually beneficial integration of heterogeneous information is the cornerstone towards the success of CTR prediction.
no code implementations • 14 Nov 2024 • Xinyu He, Jose Sepulveda, Mostafa Rahmani, Alyssa Woo, Fei Wang, Hanghang Tong
Due to the difficulty of acquiring large-scale explicit user feedback, implicit feedback (e. g., clicks or other interactions) is widely applied as an alternative source of data, where user-item interactions can be modeled as a bipartite graph.
1 code implementation • 4 Nov 2024 • Ruizhong Qiu, Zhe Xu, Wenxuan Bao, Hanghang Tong
In this work, we show that prompting is in fact Turing-complete: there exists a finite-size Transformer such that for any computable function, there exists a corresponding prompt following which the Transformer computes the function.
1 code implementation • 3 Nov 2024 • Yikun Ban, Jiaru Zou, Zihao Li, Yunzhe Qi, Dongqi Fu, Jian Kang, Hanghang Tong, Jingrui He
Link prediction is a critical problem in graph learning with broad applications such as recommender systems and knowledge graph completion.
no code implementations • 16 Oct 2024 • Dongqi Fu, Liri Fang, Zihao Li, Hanghang Tong, Vetle I. Torvik, Jingrui He
Graphs have been widely used in the past decades of big data and AI to model comprehensive relational data.
no code implementations • 9 Oct 2024 • Wenxuan Bao, Zhichen Zeng, Zhining Liu, Hanghang Tong, Jingrui He
However, existing TTA algorithms are primarily designed for attribute shifts in vision tasks, where samples are independent.
no code implementations • 3 Oct 2024 • Zhe Xu, Kaveh Hassani, Si Zhang, Hanqing Zeng, Michihiro Yasunaga, Limei Wang, Dongqi Fu, Ning Yao, Bo Long, Hanghang Tong
Language Models (LMs) are increasingly challenging the dominance of domain-specific models, such as Graph Neural Networks (GNNs) and Graph Transformers (GTs), in graph learning tasks.
1 code implementation • 3 Oct 2024 • Xiao Lin, Zhining Liu, Dongqi Fu, Ruizhong Qiu, Hanghang Tong
Multivariate Time Series (MTS) forecasting is a fundamental task with numerous real-world applications, such as transportation, climate, and epidemiology.
1 code implementation • 16 Sep 2024 • Hezhe Qiao, Hanghang Tong, Bo An, Irwin King, Charu Aggarwal, Guansong Pang
To this end, in this work we aim to present a comprehensive review of deep learning approaches for GAD.
no code implementations • 8 Aug 2024 • Dongqi Fu, Yada Zhu, Hanghang Tong, Kommy Weldemariam, Onkar Bhardwaj, Jingrui He
Understanding the causal interaction of time series variables can contribute to time series data analysis for many real-world applications, such as climate forecasting and extreme weather alerts.
no code implementations • 23 Jun 2024 • Zexing Xu, Linjun Zhang, Sitan Yang, Rasoul Etesami, Hanghang Tong, huan zhang, Jiawei Han
In this paper, we propose a novel approach that leverages strategically chosen proxy data reflective of potential sales patterns from similar entities during non-peak periods, enriched by features learned from a graph neural networks (GNNs)-based forecasting model, to predict demand during peak events.
1 code implementation • 23 Jun 2024 • Xiaodong Yang, Huiyuan Chen, Yuchen Yan, Yuxin Tang, Yuying Zhao, Eric Xu, Yiwei Cai, Hanghang Tong
The learning objective is integral to collaborative filtering systems, where the Bayesian Personalized Ranking (BPR) loss is widely used for learning informative backbones.
1 code implementation • 13 Jun 2024 • Zhining Liu, Ruizhong Qiu, Zhichen Zeng, Yada Zhu, Hendrik Hamann, Hanghang Tong
Data collected in the real world often encapsulates historical discrimination against disadvantaged groups and individuals.
1 code implementation • 10 Jun 2024 • Ruizhong Qiu, Weiliang Will Zeng, James Ezick, Christopher Lott, Hanghang Tong
Secondly, to set a high-standard for efficiency evaluation, we employ a human expert to design best algorithms and implementations as our reference solutions of efficiency, many of which are much more efficient than existing canonical solutions in HumanEval and HumanEval+.
1 code implementation • 27 May 2024 • Ruizhong Qiu, Hanghang Tong
In this paper, we propose *Gradient Compressed Sensing* (GraCe), a query-efficient and accurate estimator for sparse gradients that uses only $O\big(s\log\log\frac ds\big)$ queries per step and still achieves $O\big(\frac1T\big)$ rate of convergence.
1 code implementation • 19 May 2024 • Zhe Xu, Ruizhong Qiu, Yuzhong Chen, Huiyuan Chen, Xiran Fan, Menghai Pan, Zhichen Zeng, Mahashweta Das, Hanghang Tong
Graph is a prevalent discrete data structure, whose generation has wide applications such as drug discovery and circuit design.
no code implementations • 7 May 2024 • Huiyuan Chen, Zhe Xu, Chin-Chia Michael Yeh, Vivian Lai, Yan Zheng, Minghua Xu, Hanghang Tong
Graph Transformers have garnered significant attention for learning graph-structured data, thanks to their superb ability to capture long-range dependencies among nodes.
1 code implementation • 23 Apr 2024 • Yao Xu, Shizhu He, Jiabei Chen, ZiHao Wang, Yangqiu Song, Hanghang Tong, Guang Liu, Kang Liu, Jun Zhao
To simulate these real-world scenarios and evaluate the ability of LLMs to integrate internal and external knowledge, we propose leveraging LLMs for QA under Incomplete Knowledge Graph (IKGQA), where the provided KG lacks some of the factual triples for each question, and construct corresponding datasets.
no code implementations • 18 Apr 2024 • Yikun Ban, Ishika Agarwal, Ziwei Wu, Yada Zhu, Kommy Weldemariam, Hanghang Tong, Jingrui He
We study both stream-based and pool-based active learning with neural network approximations.
1 code implementation • 30 Mar 2024 • Lecheng Zheng, Baoyu Jing, Zihao Li, Hanghang Tong, Jingrui He
In the era of big data and Artificial Intelligence, an emerging paradigm is to utilize contrastive self-supervised learning to model large-scale heterogeneous data.
1 code implementation • 17 Mar 2024 • Lihui Liu, ZiHao Wang, Ruizhong Qiu, Yikun Ban, Eunice Chan, Yangqiu Song, Jingrui He, Hanghang Tong
Through the utilization of both knowledge graph reasoning and LLMs, it successfully derives answers for each subquestion.
no code implementations • 3 Mar 2024 • Weizhi Fei, ZiHao Wang, Hang Yin, Yang Duan, Hanghang Tong, Yangqiu Song
To bridge this gap, we study the setting of soft queries on uncertain knowledge, which is motivated by the establishment of soft constraint programming.
no code implementations • 28 Feb 2024 • Qineng Wang, ZiHao Wang, Ying Su, Hanghang Tong, Yangqiu Song
Recent progress in LLMs discussion suggests that multi-agent discussion improves the reasoning abilities of LLMs.
no code implementations • 26 Feb 2024 • Weilin Cong, Jian Kang, Hanghang Tong, Mehrdad Mahdavi
Temporal Graph Learning (TGL) has become a prevalent technique across diverse real-world applications, especially in domains where data can be represented as a graph and evolves over time.
no code implementations • 27 Dec 2023 • Lihui Liu, Blaine Hill, Boxin Du, Fei Wang, Hanghang Tong
CornNet adopts a teacher-student architecture where a teacher model learns question representations using human writing reformulations, and a student model to mimic the teacher model's output via reformulations generated by LLMs.
no code implementations • 13 Dec 2023 • Zhe Xu, Menghai Pan, Yuzhong Chen, Huiyuan Chen, Yuchen Yan, Mahashweta Das, Hanghang Tong
In the context of graph machine learning, graph rationale is defined to locate the critical subgraph in the given graph topology.
1 code implementation • 5 Nov 2023 • Yushun Dong, Binchi Zhang, Hanghang Tong, Jundong Li
Graph Neural Networks (GNNs) have emerged as a prominent graph learning model in various graph-based tasks over the years.
1 code implementation • 24 Oct 2023 • Jian Kang, Yinglong Xia, Ross Maciejewski, Jiebo Luo, Hanghang Tong
We study deceptive fairness attacks on graphs to answer the following question: How can we achieve poisoning attacks on a graph learning model to exacerbate the bias deceptively?
no code implementations • 6 Oct 2023 • Zhichen Zeng, Boxin Du, Si Zhang, Yinglong Xia, Zhining Liu, Hanghang Tong
To depict high-order relationships across multiple networks, the FGW distance is generalized to the multi-marginal setting, based on which networks can be aligned jointly.
1 code implementation • 4 Oct 2023 • ZiHao Wang, Yongqiang Chen, Yang Duan, Weijiang Li, Bo Han, James Cheng, Hanghang Tong
Under this framework, we create comprehensive datasets to benchmark (1) the state-of-the-art ML approaches for reaction prediction in the OOD setting and (2) the state-of-the-art graph OOD methods in kinetics property prediction problems.
no code implementations • 23 Sep 2023 • Haibo Ye, Xinjie Li, Yuan YAO, Hanghang Tong
In recommender systems, knowledge graph (KG) can offer critical information that is lacking in the original user-item interaction graph (IG).
no code implementations • 15 Sep 2023 • Marinka Zitnik, Michelle M. Li, Aydin Wells, Kimberly Glass, Deisy Morselli Gysi, Arjun Krishnan, T. M. Murali, Predrag Radivojac, Sushmita Roy, Anaïs Baudot, Serdar Bozdag, Danny Z. Chen, Lenore Cowen, Kapil Devkota, Anthony Gitter, Sara Gosline, Pengfei Gu, Pietro H. Guzzi, Heng Huang, Meng Jiang, Ziynet Nesibe Kesimoglu, Mehmet Koyuturk, Jian Ma, Alexander R. Pico, Nataša Pržulj, Teresa M. Przytycka, Benjamin J. Raphael, Anna Ritz, Roded Sharan, Yang shen, Mona Singh, Donna K. Slonim, Hanghang Tong, Xinan Holly Yang, Byung-Jun Yoon, Haiyuan Yu, Tijana Milenković
Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales.
1 code implementation • 11 Sep 2023 • Yunyong Ko, Hanghang Tong, Sang-Wook Kim
To tackle both challenges together, in this paper, we propose a novel hyperedge prediction framework (CASH) that employs (1) context-aware node aggregation to precisely capture complex relations among nodes in each hyperedge for (C1) and (2) self-supervised contrastive learning in the context of hyperedge prediction to enhance hypergraph representations for (C2).
no code implementations • 29 Aug 2023 • Hyunsik Yoo, Zhichen Zeng, Jian Kang, Ruizhong Qiu, David Zhou, Zhining Liu, Fei Wang, Charlie Xu, Eunice Chan, Hanghang Tong
In the ever-evolving landscape of user-item interactions, continual adaptation to newly collected data is crucial for recommender systems to stay aligned with the latest user preferences.
1 code implementation • 27 Aug 2023 • Zhining Liu, Ruizhong Qiu, Zhichen Zeng, Hyunsik Yoo, David Zhou, Zhe Xu, Yada Zhu, Kommy Weldemariam, Jingrui He, Hanghang Tong
In this work, we approach the root cause of class-imbalance bias from an topological paradigm.
no code implementations • 10 Jul 2023 • Dongqi Fu, Wenxuan Bao, Ross Maciejewski, Hanghang Tong, Jingrui He
We systematically review related works from the data to the computational aspects.
no code implementations • 13 Jun 2023 • Ruijie Wang, Baoyu Li, Yichen Lu, Dachun Sun, Jinning Li, Yuchen Yan, Shengzhong Liu, Hanghang Tong, Tarek F. Abdelzaher
State-of-the-art methods fall short in the speculative reasoning ability, as they assume the correctness of a fact is solely determined by its presence in KG, making them vulnerable to false negative/positive issues.
1 code implementation • 7 Jun 2023 • Xiao Lin, Jian Kang, Weilin Cong, Hanghang Tong
Fairness in graph neural networks has been actively studied recently.
1 code implementation • 1 Jun 2023 • Ruizhong Qiu, Dingsu Wang, Lei Ying, H. Vincent Poor, Yifang Zhang, Hanghang Tong
They are exclusively based on the maximum likelihood estimation (MLE) formulation and require to know true diffusion parameters.
no code implementations • 29 May 2023 • Dingsu Wang, Yuchen Yan, Ruizhong Qiu, Yada Zhu, Kaiyu Guan, Andrew J Margenot, Hanghang Tong
First, we define the problem of imputation over NTS which contains missing values in both node time series features and graph structures.
1 code implementation • 22 May 2023 • Chi Han, Qizheng He, Charles Yu, Xinya Du, Hanghang Tong, Heng Ji
A LERP is designed as a vector of probabilistic logical functions on the entity's neighboring sub-graph.
Ranked #11 on
Link Prediction
on WN18RR
no code implementations • 11 Apr 2023 • Boxin Du, Lihui Liu, Jiejun Xu, Fei Wang, Hanghang Tong
Graph Neural Networks (GNNs) have been widely applied on a variety of real-world applications, such as social recommendation.
no code implementations • 30 Mar 2023 • Lecheng Zheng, Dawei Zhou, Hanghang Tong, Jiejun Xu, Yada Zhu, Jingrui He
In addition, we propose a generic context sampling strategy for graph generative models, which is proven to be capable of fairly capturing the contextual information of each group with a high probability.
1 code implementation • 23 Feb 2023 • Yunyong Ko, Seongeun Ryu, Soeun Han, Youngseung Jeon, JaeHoon Kim, SoHyun Park, Kyungsik Han, Hanghang Tong, Sang-Wook Kim
Motivated by this, in this work, we conduct a user study to investigate important factors in political stance prediction, and observe that the context and tone of a news article (implicit) and external knowledge for real-world entities appearing in the article (explicit) are important in determining its political stance.
no code implementations • 22 Feb 2023 • Weilin Cong, Si Zhang, Jian Kang, Baichuan Yuan, Hao Wu, Xin Zhou, Hanghang Tong, Mehrdad Mahdavi
Recurrent neural network (RNN) and self-attention mechanism (SAM) are the de facto methods to extract spatial-temporal information for temporal graph learning.
no code implementations • 25 Jan 2023 • Baoyu Jing, Yuchen Yan, Kaize Ding, Chanyoung Park, Yada Zhu, Huan Liu, Hanghang Tong
Most recent bipartite graph SSL methods are based on contrastive learning which learns embeddings by discriminating positive and negative node pairs.
no code implementations • 19 Jan 2023 • Shengyu Feng, Hanghang Tong
The advances in deep learning have enabled machine learning methods to outperform human beings in various areas, but it remains a great challenge for a well-trained model to quickly adapt to a new task.
no code implementations • 8 Nov 2022 • Chuxuan Hu, Qinghai Zhou, Hanghang Tong
Subteam replacement is defined as finding the optimal candidate set of people who can best function as an unavailable subset of members (i. e., subteam) for certain reasons (e. g., conflicts of interests, employee churn), given a team of people embedded in a social network working on the same task.
no code implementations • 12 Oct 2022 • Jian Kang, Qinghai Zhou, Hanghang Tong
The proposed JuryGCN is capable of quantifying uncertainty deterministically without modifying the GCN architecture or introducing additional parameters.
no code implementations • 4 Oct 2022 • Haipeng Luo, Hanghang Tong, Mengxiao Zhang, Yuheng Zhang
For general strongly observable graphs, we develop an algorithm that achieves the optimal regret $\widetilde{\mathcal{O}}((\sum_{t=1}^T\alpha_t)^{1/2}+\max_{t\in[T]}\alpha_t)$ with high probability, where $\alpha_t$ is the independence number of the feedback graph at round $t$.
1 code implementation • 2 Oct 2022 • Yikun Ban, Yuheng Zhang, Hanghang Tong, Arindam Banerjee, Jingrui He
We improve the theoretical and empirical performance of neural-network(NN)-based active learning algorithms for the non-parametric streaming setting.
2 code implementations • 1 Oct 2022 • Wenhao Ding, Qing He, Hanghang Tong, Qingjing Wang, Ping Wang
This framework integrates engineering dynamics and deep learning technologies and may reveal a new concept for CDEs solving and uncertainty propagation.
no code implementations • 27 Sep 2022 • Baoyu Jing, Si Zhang, Yada Zhu, Bin Peng, Kaiyu Guan, Andrew Margenot, Hanghang Tong
In this paper, we show both theoretically and empirically that the uncertainty could be effectively reduced by retrieving relevant time series as references.
1 code implementation • 15 Aug 2022 • Shengyu Feng, Baoyu Jing, Yada Zhu, Hanghang Tong
In this work, by introducing an adversarial graph view for data augmentation, we propose a simple but effective method, Adversarial Graph Contrastive Learning (ARIEL), to extract informative contrastive samples within reasonable constraints.
no code implementations • 30 Jun 2022 • Dongqi Fu, Jingrui He, Hanghang Tong, Ross Maciejewski
Directly motivated by security-related applications from the Homeland Security Enterprise, we focus on the privacy-preserving analysis of graph data, which provides the crucial capacity to represent rich attributes and relationships.
no code implementations • 19 Jun 2022 • Boxin Du, Changhe Yuan, Fei Wang, Hanghang Tong
Despite the success of the Sylvester equation empowered methods on various graph mining applications, such as semi-supervised label learning and network alignment, there also exists several limitations.
no code implementations • 6 Jun 2022 • Hongwei Wang, Zixuan Zhang, Sha Li, Jiawei Han, Yizhou Sun, Hanghang Tong, Joseph P. Olive, Heng Ji
Existing link prediction or graph completion methods have difficulty dealing with event graphs because they are usually designed for a single large graph such as a social network or a knowledge graph, rather than multiple small dynamic event graphs.
no code implementations • 1 Jun 2022 • Haoran Li, Yang Weng, Hanghang Tong
In the first step of searching for right symbols, we convexify the deep Q-learning.
no code implementations • 31 May 2022 • Baoyu Jing, Yuchen Yan, Yada Zhu, Hanghang Tong
We theoretically prove that COIN is able to effectively increase the mutual information of node embeddings and COIN is upper-bounded by the prior distributions of nodes.
no code implementations • 16 May 2022 • He Zhang, Bang Wu, Xingliang Yuan, Shirui Pan, Hanghang Tong, Jian Pei
Graph neural networks (GNNs) have emerged as a series of competent graph learning methods for diverse real-world scenarios, ranging from daily applications like recommendation systems and question answering to cutting-edge technologies such as drug discovery in life sciences and n-body simulation in astrophysics.
no code implementations • 6 May 2022 • Beidi Zhao, Boxin Du, Zhe Xu, Liangyue Li, Hanghang Tong
Graph Neural Networks (GNNs) have achieved tremendous success in a variety of real-world applications by relying on the fixed graph data as input.
no code implementations • 5 May 2022 • Zhenning Zhang, Boxin Du, Hanghang Tong
Bundle recommendation is an emerging research direction in the recommender system with the focus on recommending customized bundles of items for users.
no code implementations • 21 Apr 2022 • Senrong Xu, Yuan YAO, Liangyue Li, Wei Yang, Feng Xu, Hanghang Tong
In this work, we study the victim node detection problem under topology attacks against GNNs.
1 code implementation • 9 Mar 2022 • Dongqi Fu, Yikun Ban, Hanghang Tong, Ross Maciejewski, Jingrui He
Disinformation refers to false information deliberately spread to influence the general public, and the negative impact of disinformation on society can be observed in numerous issues, such as political agendas and manipulating financial markets.
no code implementations • 28 Feb 2022 • Jian Kang, Yan Zhu, Yinglong Xia, Jiebo Luo, Hanghang Tong
Graph Convolutional Network (GCN) plays pivotal roles in many real-world applications.
1 code implementation • 16 Feb 2022 • Kaize Ding, Zhe Xu, Hanghang Tong, Huan Liu
In this survey, we formally formulate the problem of graph data augmentation and further review the representative techniques and their applications in different deep graph learning problems.
1 code implementation • 14 Feb 2022 • Shengyu Feng, Baoyu Jing, Yada Zhu, Hanghang Tong
Contrastive learning is an effective unsupervised method in graph representation learning.
1 code implementation • 24 Nov 2021 • Zhining Liu, Jian Kang, Hanghang Tong, Yi Chang
imbalanced-ensemble, abbreviated as imbens, is an open-source Python toolbox for leveraging the power of ensemble learning to address the class imbalance problem.
1 code implementation • 22 Nov 2021 • Tong Wang, Yuan YAO, Feng Xu, Shengwei An, Hanghang Tong, Ting Wang
We also evaluate FTROJAN against state-of-the-art defenses as well as several adaptive defenses that are designed on the frequency domain.
1 code implementation • 28 Oct 2021 • Bolian Li, Baoyu Jing, Hanghang Tong
We argue that the community information should be considered to identify node pairs in the same communities, where the nodes insides are semantically similar.
no code implementations • 16 Oct 2021 • Haonan Wang, Wei Huang, Ziwei Wu, Andrew Margenot, Hanghang Tong, Jingrui He
Active learning theories and methods have been extensively studied in classical statistical learning settings.
no code implementations • 8 Oct 2021 • Wei Du, Xintao Wu, Hanghang Tong
However, all previous fair regression research assumed the training data and testing data are drawn from the same distributions.
1 code implementation • 1 Oct 2021 • Jinning Li, Huajie Shao, Dachun Sun, Ruijie Wang, Yuchen Yan, Jinyang Li, Shengzhong Liu, Hanghang Tong, Tarek Abdelzaher
Inspired by total correlation in information theory, we propose the Information-Theoretic Variational Graph Auto-Encoder (InfoVGAE) that learns to project both users and content items (e. g., posts that represent user views) into an appropriate disentangled latent space.
no code implementations • 8 Sep 2021 • Baoyu Jing, Shengyu Feng, Yuejia Xiang, Xi Chen, Yu Chen, Hanghang Tong
X-GOAL is comprised of two components: the GOAL framework, which learns node embeddings for each homogeneous graph layer, and an alignment regularization, which jointly models different layers by aligning layer-specific node embeddings.
no code implementations • EMNLP 2021 • Baoyu Jing, Zeyu You, Tao Yang, Wei Fan, Hanghang Tong
Extractive text summarization aims at extracting the most representative sentences from a given document as its summary.
1 code implementation • NAACL 2021 • Haoyang Wen, Yanru Qu, Heng Ji, Qiang Ning, Jiawei Han, Avi Sil, Hanghang Tong, Dan Roth
Grounding events into a precise timeline is important for natural language understanding but has received limited attention in recent work.
no code implementations • 24 May 2021 • Jian Kang, Tiankai Xie, Xintao Wu, Ross Maciejewski, Hanghang Tong
The vast majority of the existing works on group fairness, with a few exceptions, primarily focus on debiasing with respect to a single sensitive attribute, despite the fact that the co-existence of multiple sensitive attributes (e. g., gender, race, marital status, etc.)
no code implementations • 23 May 2021 • Boxin Du, Changhe Yuan, Robert Barton, Tal Neiman, Hanghang Tong
Despite the prevalence of hypergraphs in a variety of high-impact applications, there are relatively few works on hypergraph representation learning, most of which primarily focus on hyperlink prediction, often restricted to the transductive learning setting.
no code implementations • 19 May 2021 • Zhe Xu, Boxin Du, Hanghang Tong
Generally speaking, the vast majority of the existing works aim to answer the following question, that is, given a graph, what is the best way to mine it?
2 code implementations • 22 Feb 2021 • Kaize Ding, Qinghai Zhou, Hanghang Tong, Huan Liu
Network anomaly detection aims to find network elements (e. g., nodes, edges, subgraphs) with significantly different behaviors from the vast majority.
1 code implementation • 15 Feb 2021 • Baoyu Jing, Chanyoung Park, Hanghang Tong
To address the above-mentioned problems, we propose a novel framework, called High-order Deep Multiplex Infomax (HDMI), for learning node embedding on multiplex networks in a self-supervised way.
1 code implementation • 15 Feb 2021 • Baoyu Jing, Hanghang Tong, Yada Zhu
We propose a novel model called Network of Tensor Time Series, which is comprised of two modules, including Tensor Graph Convolutional Network (TGCN) and Tensor Recurrent Neural Network (TRNN).
1 code implementation • NeurIPS 2020 • Long Chen, Yuan YAO, Feng Xu, Miao Xu, Hanghang Tong
Collaborative filtering has been widely used in recommender systems.
no code implementations • 6 Nov 2020 • Lihui Liu, Boxin Du, Heng Ji, Hanghang Tong
In detail, we develop KompaRe, the first of its kind prototype system that provides comparative reasoning capability over large knowledge graphs.
no code implementations • 10 Oct 2020 • Wei Du, Depeng Xu, Xintao Wu, Hanghang Tong
In this paper, we develop a fairness-aware agnostic federated learning framework (AgnosticFair) to deal with the challenge of unknown testing distribution.
no code implementations • 17 Aug 2020 • Jiaying Liu, Feng Xia, Lei Wang, Bo Xu, Xiangjie Kong, Hanghang Tong, Irwin King
The advisor-advisee relationship represents direct knowledge heritage, and such relationship may not be readily available from academic libraries and search engines.
1 code implementation • 10 Jun 2020 • Scott Freitas, Diyi Yang, Srijan Kumar, Hanghang Tong, Duen Horng Chau
By democratizing the tools required to study network robustness, our goal is to assist researchers and practitioners in analyzing their own networks; and facilitate the development of new research in the field.
no code implementations • NeurIPS 2019 • Rui Zhang, Hanghang Tong
More significantly, the degree of the sparsity is steerable such that only exact k well-fitting samples with least reconstruction errors are activated during the optimization, while the residual samples, i. e., the extreme noised ones are eliminated for the global robustness.
no code implementations • NeurIPS 2019 • Yongkai Wu, Lu Zhang, Xintao Wu, Hanghang Tong
A recent trend of fair machine learning is to define fairness as causality-based notions which concern the causal connection between protected attributes and decisions.
no code implementations • 29 Jul 2019 • Zhuochen Jin, Nan Cao, Yang Shi, Hanghang Tong, Yingcai Wu
A suite of visualizations is designed to illustrate the dynamics of city segmentation and the corresponding interactions are added to support the exploration of the segmentation patterns over time.
no code implementations • 14 May 2019 • Rui Zhang, Luca Giancardo, Danilo A. Pena, Yejin Kim, Hanghang Tong, Xiaoqian Jiang
In this paper, we studied the association between the change of structural brain volumes to the potential development of Alzheimer's disease (AD).
no code implementations • 14 May 2019 • Yang Shi, Yuyin Liu, Hanghang Tong, Jingrui He, Gang Yan, Nan Cao
The increasing accessibility of data provides substantial opportunities for understanding user behaviors.
2 code implementations • 10 Apr 2015 • Jing Zhang, Jie Tang, Cong Ma, Hanghang Tong, Yu Jing, Juanzi Li
The algorithm is based on a novel idea of random path, and an extended method is also presented, to enhance the structural similarity when two vertices are completely disconnected.
Social and Information Networks
no code implementations • 3 Apr 2015 • Liangyue Li, Hanghang Tong
Understanding the dynamic mechanisms that drive the high-impact scientific work (e. g., research papers, patents) is a long-debated research topic and has many important implications, ranging from personal career development and recruitment search, to the jurisdiction of research resources.
no code implementations • 3 Sep 2014 • Fanhua Shang, Yuanyuan Liu, Hanghang Tong, James Cheng, Hong Cheng
In this paper, we propose a scalable, provable structured low-rank matrix factorization method to recover low-rank and sparse matrices from missing and grossly corrupted data, i. e., robust matrix completion (RMC) problems, or incomplete and grossly corrupted measurements, i. e., compressive principal component pursuit (CPCP) problems.
1 code implementation • 18 Apr 2014 • Leman Akoglu, Hanghang Tong, Danai Koutra
This survey aims to provide a general, comprehensive, and structured overview of the state-of-the-art methods for anomaly detection in data represented as graphs.
Social and Information Networks Cryptography and Security
no code implementations • 27 Nov 2013 • Yuan Yao, Hanghang Tong, Tao Xie, Leman Akoglu, Feng Xu, Jian Lu
Community Question Answering (CQA) websites have become valuable repositories which host a massive volume of human knowledge.
no code implementations • NeurIPS 2012 • Jingrui He, Hanghang Tong, Qiaozhu Mei, Boleslaw Szymanski
In this paper, we consider a generic setting where we aim to diversify the top-k ranking list based on an arbitrary relevance function and an arbitrary similarity function among all the examples.