Search Results for author: Charu Aggarwal

Found 36 papers, 10 papers with code

Leveraging Opposite Gender Interaction Ratio as a Path towards Fairness in Online Dating Recommendations Based on User Sexual Orientation

no code implementations19 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).

Fairness Recommendation Systems +1

Precedence-Constrained Winter Value for Effective Graph Data Valuation

no code implementations2 Feb 2024 Hongliang Chi, Jin Wei, Charu Aggarwal, Yao Ma

Data valuation is essential for quantifying data's worth, aiding in assessing data quality and determining fair compensation.

Data Valuation

Fairness and Diversity in Recommender Systems: A Survey

no code implementations10 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.

Fairness Recommendation Systems

A Survey on Explainability of Graph Neural Networks

no code implementations2 Jun 2023 Jaykumar Kakkad, Jaspal Jannu, Kartik Sharma, Charu Aggarwal, Sourav Medya

Graph neural networks (GNNs) are powerful graph-based deep-learning models that have gained significant attention and demonstrated remarkable performance in various domains, including natural language processing, drug discovery, and recommendation systems.

Drug Discovery Recommendation Systems

Counterfactual Learning on Graphs: A Survey

1 code implementation3 Apr 2023 Zhimeng Guo, Teng Xiao, Charu Aggarwal, Hui Liu, Suhang Wang

To facilitate the development of this promising direction, in this survey, we categorize and comprehensively review papers on graph counterfactual learning.

counterfactual Fairness +2

State of the Art and Potentialities of Graph-level Learning

no code implementations14 Jan 2023 Zhenyu Yang, Ge Zhang, Jia Wu, Jian Yang, Quan Z. Sheng, Shan Xue, Chuan Zhou, Charu Aggarwal, Hao Peng, Wenbin Hu, Edwin Hancock, Pietro Liò

Traditional approaches to learning a set of graphs heavily rely on hand-crafted features, such as substructures.

Graph Learning

Link Prediction on Heterophilic Graphs via Disentangled Representation Learning

no code implementations3 Aug 2022 Shijie Zhou, Zhimeng Guo, Charu Aggarwal, Xiang Zhang, Suhang Wang

Therefore, in this paper, we study a novel problem of exploring disentangled representation learning for link prediction on heterophilic graphs.

Link Prediction Representation Learning

Feature Overcorrelation in Deep Graph Neural Networks: A New Perspective

1 code implementation15 Jun 2022 Wei Jin, Xiaorui Liu, Yao Ma, Charu Aggarwal, Jiliang Tang

In this paper, we propose a new perspective to look at the performance degradation of deep GNNs, i. e., feature overcorrelation.

Drug Discovery

Graph-level Neural Networks: Current Progress and Future Directions

no code implementations31 May 2022 Ge Zhang, Jia Wu, Jian Yang, Shan Xue, Wenbin Hu, Chuan Zhou, Hao Peng, Quan Z. Sheng, Charu Aggarwal

To frame this survey, we propose a systematic taxonomy covering GLNNs upon deep neural networks, graph neural networks, and graph pooling.

Sequential/Session-based Recommendations: Challenges, Approaches, Applications and Opportunities

no code implementations22 May 2022 Shoujin Wang, Qi Zhang, Liang Hu, Xiuzhen Zhang, Yan Wang, Charu Aggarwal

In recent years, sequential recommender systems (SRSs) and session-based recommender systems (SBRSs) have emerged as a new paradigm of RSs to capture users' short-term but dynamic preferences for enabling more timely and accurate recommendations.

Session-Based Recommendations

Graph Lifelong Learning: A Survey

no code implementations22 Feb 2022 Falih Gozi Febrinanto, Feng Xia, Kristen Moore, Chandra Thapa, Charu Aggarwal

Lifelong learning methods that enable continuous learning in regular domains like images and text cannot be directly applied to continuously evolving graph data, due to its irregular structure.

Graph Learning Recommendation Systems

Distance-wise Prototypical Graph Neural Network in Node Imbalance Classification

1 code implementation22 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.

Classification Metric Learning +2

Towards Feature Overcorrelation in Deeper Graph Neural Networks

no code implementations29 Sep 2021 Wei Jin, Xiaorui Liu, Yao Ma, Charu Aggarwal, Jiliang Tang

In this paper, we observe a new issue in deeper GNNs, i. e., feature overcorrelation, and perform a thorough study to deepen our understanding on this issue.

Graph Representation Learning

Jointly Attacking Graph Neural Network and its Explanations

no code implementations7 Aug 2021 Wenqi Fan, Wei Jin, Xiaorui Liu, Han Xu, Xianfeng Tang, Suhang Wang, Qing Li, Jiliang Tang, JianPing Wang, Charu Aggarwal

Despite the great success, recent studies have shown that GNNs are highly vulnerable to adversarial attacks, where adversaries can mislead the GNNs' prediction by modifying graphs.

NRGNN: Learning a Label Noise-Resistant Graph Neural Network on Sparsely and Noisily Labeled Graphs

1 code implementation8 Jun 2021 Enyan Dai, Charu Aggarwal, Suhang Wang

Graph Neural Networks (GNNs) have achieved promising results for semi-supervised learning tasks on graphs such as node classification.

Node Classification

Graph Feature Gating Networks

no code implementations10 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.


SetConv: A New Approach for Learning from Imbalanced Data

no code implementations EMNLP 2020 Yang Gao, Yi-Fan Li, Yu Lin, Charu Aggarwal, Latifur Khan

For many real-world classification problems, e. g., sentiment classification, most existing machine learning methods are biased towards the majority class when the Imbalance Ratio (IR) is high.

BIG-bench Machine Learning Classification +3

Meta-Learning with Graph Neural Networks: Methods and Applications

no code implementations27 Feb 2021 Debmalya Mandal, Sourav Medya, Brian Uzzi, Charu Aggarwal

Graph Neural Networks (GNNs), a generalization of deep neural networks on graph data have been widely used in various domains, ranging from drug discovery to recommender systems.

Drug Discovery Meta-Learning +1

TINKER: A framework for Open source Cyberthreat Intelligence

no code implementations10 Feb 2021 Nidhi Rastogi, Sharmishtha Dutta, Mohammed J. Zaki, Alex Gittens, Charu Aggarwal

The information is extracted and stored in a structured format using knowledge graphs such that the semantics of the threat intelligence can be preserved and shared at scale with other security analysts.

Information Retrieval Intrusion Detection +3

Investigating and Mitigating Degree-Related Biases in Graph Convolutional Networks

no code implementations28 Jun 2020 Xianfeng Tang, Huaxiu Yao, Yiwei Sun, Yiqi Wang, Jiliang Tang, Charu Aggarwal, Prasenjit Mitra, Suhang Wang

Pseudo labels increase the chance of connecting to labeled neighbors for low-degree nodes, thus reducing the biases of GCNs from the data perspective.

Self-Supervised Learning

MALOnt: An Ontology for Malware Threat Intelligence

1 code implementation20 Jun 2020 Nidhi Rastogi, Sharmishtha Dutta, Mohammed J. Zaki, Alex Gittens, Charu Aggarwal

The knowledge graph that uses MALOnt is instantiated from a corpus comprising hundreds of annotated malware threat reports.

Decision Making Graph Generation +1

Customized Graph Neural Networks

no code implementations22 May 2020 Yiqi Wang, Yao Ma, Wei Jin, Chaozhuo Li, Charu Aggarwal, Jiliang Tang

Therefore, in this paper, we aim to develop customized graph neural networks for graph classification.

General Classification Graph Classification

Adversarial Attacks and Defenses on Graphs: A Review, A Tool and Empirical Studies

3 code implementations2 Mar 2020 Wei Jin, Ya-Xin Li, Han Xu, Yiqi Wang, Shuiwang Ji, Charu Aggarwal, Jiliang Tang

As the extensions of DNNs to graphs, Graph Neural Networks (GNNs) have been demonstrated to inherit this vulnerability.

Adversarial Attack

Bridging the Gap between Spatial and Spectral Domains: A Survey on Graph Neural Networks

no code implementations27 Feb 2020 Zhiqian Chen, Fanglan Chen, Lei Zhang, Taoran Ji, Kaiqun Fu, Liang Zhao, Feng Chen, Lingfei Wu, Charu Aggarwal, Chang-Tien Lu

Deep learning's success has been widely recognized in a variety of machine learning tasks, including image classification, audio recognition, and natural language processing.

Image Classification Natural Language Understanding +1

Efficient Global String Kernel with Random Features: Beyond Counting Substructures

no code implementations25 Nov 2019 Lingfei Wu, Ian En-Hsu Yen, Siyu Huo, Liang Zhao, Kun Xu, Liang Ma, Shouling Ji, Charu Aggarwal

In this paper, we present a new class of global string kernels that aims to (i) discover global properties hidden in the strings through global alignments, (ii) maintain positive-definiteness of the kernel, without introducing a diagonal dominant kernel matrix, and (iii) have a training cost linear with respect to not only the length of the string but also the number of training string samples.

Stock Price Prediction via Discovering Multi-Frequency Trading Patterns

1 code implementation13 Aug 2017 Liheng Zhang, Charu Aggarwal, Guo-Jun Qi

Then the future stock prices are predicted as a nonlinear mapping of the combination of these components in an Inverse Fourier Transform (IFT) fashion.

Stock Price Prediction Time Series Analysis

Efficient Data Representation by Selecting Prototypes with Importance Weights

1 code implementation5 Jul 2017 Karthik S. Gurumoorthy, Amit Dhurandhar, Guillermo Cecchi, Charu Aggarwal

Prototypical examples that best summarizes and compactly represents an underlying complex data distribution communicate meaningful insights to humans in domains where simple explanations are hard to extract.

REMIX: Automated Exploration for Interactive Outlier Detection

no code implementations17 May 2017 Yanjie Fu, Charu Aggarwal, Srinivasan Parthasarathy, Deepak S. Turaga, Hui Xiong

This formulation incorporates multiple aspects such as (i) an upper limit on the total execution time of detectors (ii) diversity in the space of algorithms and features, and (iii) meta-learning for evaluating the cost and utility of detectors.

Meta-Learning Outlier Detection

Joint Intermodal and Intramodal Label Transfers for Extremely Rare or Unseen Classes

no code implementations22 Mar 2017 Guo-Jun Qi, Wei Liu, Charu Aggarwal, Thomas Huang

One of our goals in this paper is to develop a model for revealing the functional relationships between text and image features as to directly transfer intermodal and intramodal labels to annotate the images.

General Classification Image Classification +3

A Survey of Signed Network Mining in Social Media

no code implementations24 Nov 2015 Jiliang Tang, Yi Chang, Charu Aggarwal, Huan Liu

Many real-world relations can be represented by signed networks with positive and negative links, as a result of which signed network analysis has attracted increasing attention from multiple disciplines.

Node Classification in Uncertain Graphs

no code implementations22 May 2014 Michele Dallachiesa, Charu Aggarwal, Themis Palpanas

We study the novel problem of node classification in uncertain graphs, by treating uncertainty as a first-class citizen.

Classification General Classification +1

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