no code implementations • 15 May 2024 • Zaitian Wang, Pengfei Wang, Kunpeng Liu, Pengyang Wang, Yanjie Fu, Chang-Tien Lu, Charu C. Aggarwal, Jian Pei, Yuanchun Zhou
Existing literature surveys only focus on a certain type of specific modality data, and categorize these methods from modality-specific and operation-centric perspectives, which lacks a consistent summary of data augmentation methods across multiple modalities and limits the comprehension of how existing data samples serve the data augmentation process.
no code implementations • 13 Feb 2024 • Jin Li, Shoujin Wang, Qi Zhang, Longbing Cao, Fang Chen, Xiuzhen Zhang, Dietmar Jannach, Charu C. Aggarwal
However, emerging vulnerabilities in RS have catalyzed a paradigm shift towards Trustworthy RS (TRS).
1 code implementation • 2 Nov 2023 • Harry Shomer, Yao Ma, Juanhui Li, Bo Wu, Charu C. Aggarwal, Jiliang Tang
A new class of methods have been proposed to tackle this problem by aggregating path information.
no code implementations • 18 Aug 2023 • Zahra Zamanzadeh Darban, Geoffrey I. Webb, Shirui Pan, Charu C. Aggarwal, Mahsa Salehi
Our research shows the potential of contrastive representation learning to advance time series anomaly detection.
no code implementations • 3 Jun 2023 • Zhichao Hou, Xitong Zhang, Wei Wang, Charu C. Aggarwal, Xiaorui Liu
This work presents the first investigation into the robustness of GNNs in the context of directed graphs, aiming to harness the profound trust implications offered by directed graphs to bolster the robustness and resilience of GNNs.
1 code implementation • 20 Feb 2023 • Zitai Qiu, Jia Wu, Jian Yang, Xing Su, Charu C. Aggarwal
This model addresses the heterogeneity of social media, and, with this graph, the information in social media can be used to capture structural information based on the properties of hyperbolic space.
no code implementations • 11 Dec 2022 • Jing Ren, Feng Xia, Azadeh Noori Hoshyar, Charu C. Aggarwal
Anomaly analytics is a popular and vital task in various research contexts, which has been studied for several decades.
1 code implementation • 9 Nov 2022 • Zahra Zamanzadeh Darban, Geoffrey I. Webb, Shirui Pan, Charu C. Aggarwal, Mahsa Salehi
Time series anomaly detection has applications in a wide range of research fields and applications, including manufacturing and healthcare.
1 code implementation • 18 Oct 2022 • Fanzhen Liu, Xiaoxiao Ma, Jia Wu, Jian Yang, Shan Xue, Amin Beheshti, Chuan Zhou, Hao Peng, Quan Z. Sheng, Charu C. Aggarwal
To bridge the gaps, this paper devises a novel Data Augmentation-based Graph Anomaly Detection (DAGAD) framework for attributed graphs, equipped with three specially designed modules: 1) an information fusion module employing graph neural network encoders to learn representations, 2) a graph data augmentation module that fertilizes the training set with generated samples, and 3) an imbalance-tailored learning module to discriminate the distributions of the minority (anomalous) and majority (normal) classes.
no code implementations • 8 Sep 2022 • Djallel Bouneffouf, Charu C. Aggarwal
In recent years, the Neurosymbolic framework has attracted a lot of attention in various applications, from recommender systems and information retrieval to healthcare and finance.
no code implementations • RANLP 2021 • Zhiqiang Hu, Roy Ka-Wei Lee, Charu C. Aggarwal
Existing text style transfer (TST) methods rely on style classifiers to disentangle the text's content and style attributes for text style transfer.
2 code implementations • 24 Oct 2020 • Zhiqiang Hu, Roy Ka-Wei Lee, Charu C. Aggarwal, Aston Zhang
This article aims to provide a comprehensive review of recent research efforts on text style transfer.
1 code implementation • 30 Apr 2019 • Yao Ma, Suhang Wang, Charu C. Aggarwal, Jiliang Tang
To apply graph neural networks for the graph classification task, approaches to generate the \textit{graph representation} from node representations are demanded.
Ranked #1 on Graph Classification on NC1
no code implementations • 18 Aug 2018 • Yao Ma, Suhang Wang, Charu C. Aggarwal, Dawei Yin, Jiliang Tang
Convolutional neural networks (CNNs) leverage the great power in representation learning on regular grid data such as image and video.
Social and Information Networks
1 code implementation • ACM SIGKDD International Conference on Knowledge Discovery & Data Mining 2018 • Wenchao Yu, Cheng Zheng, Wei Cheng, Charu C. Aggarwal, Dongjin Song, Bo Zong, Haifeng Chen, Wei Wang
The problem of network representation learning, also known as network embedding, arises in many machine learning tasks assuming that there exist a small number of variabilities in the vertex representations which can capture the "semantics" of the original network structure.
1 code implementation • 17 Jul 2018 • Ana Paula Appel, Renato L. F. Cunha, Charu C. Aggarwal, Marcela Megumi Terakado
In this work, we consider the problem of combining link, content and temporal analysis for community detection and prediction in evolving networks.
1 code implementation • 5 Jan 2017 • Ramakrishnan Kannan, Hyenkyun Woo, Charu C. Aggarwal, Haesun Park
In such cases, it often becomes difficult to separate the outliers from the natural variations in the patterns in the underlying data.