Search Results for author: Yanfang Ye

Found 30 papers, 14 papers with code

Let Graph be the Go Board: Gradient-free Node Injection Attack for Graph Neural Networks via Reinforcement Learning

1 code implementation19 Nov 2022 Mingxuan Ju, Yujie Fan, Chuxu Zhang, Yanfang Ye

Whereas for the node injection attack, though being more practical, current approaches require training surrogate models to simulate a white-box setting, which results in significant performance downgrade when the surrogate architecture diverges from the actual victim model.

Product Recommendation

Self-Supervised Graph Structure Refinement for Graph Neural Networks

no code implementations12 Nov 2022 Jianan Zhao, Qianlong Wen, Mingxuan Ju, Chuxu Zhang, Yanfang Ye

Specifically, The pre-training phase aims to comprehensively estimate the underlying graph structure by a multi-view contrastive learning framework with both intra- and inter-view link prediction tasks.

Contrastive Learning Graph structure learning +1

Grape: Knowledge Graph Enhanced Passage Reader for Open-domain Question Answering

1 code implementation6 Oct 2022 Mingxuan Ju, Wenhao Yu, Tong Zhao, Chuxu Zhang, Yanfang Ye

In light of this, we propose a novel knowledge Graph enhanced passage reader, namely Grape, to improve the reader performance for open-domain QA.

Entity Embeddings Open-Domain Question Answering

Multi-task Self-supervised Graph Neural Networks Enable Stronger Task Generalization

no code implementations5 Oct 2022 Mingxuan Ju, Tong Zhao, Qianlong Wen, Wenhao Yu, Neil Shah, Yanfang Ye, Chuxu Zhang

Self-supervised learning (SSL) for graph neural networks (GNNs) has attracted increasing attention from the graph machine learning community in recent years, owing to its capability to learn performant node embeddings without costly label information.

Link Prediction Node Classification +4

Multi-objective Deep Data Generation with Correlated Property Control

no code implementations1 Oct 2022 Shiyu Wang, Xiaojie Guo, Xuanyang Lin, Bo Pan, Yuanqi Du, Yinkai Wang, Yanfang Ye, Ashley Ann Petersen, Austin Leitgeb, Saleh AlKhalifa, Kevin Minbiole, William Wuest, Amarda Shehu, Liang Zhao

Developing deep generative models has been an emerging field due to the ability to model and generate complex data for various purposes, such as image synthesis and molecular design.

Image Generation

Diving into Unified Data-Model Sparsity for Class-Imbalanced Graph Representation Learning

no code implementations1 Oct 2022 Chunhui Zhang, Chao Huang, Yijun Tian, Qianlong Wen, Zhongyu Ouyang, Youhuan Li, Yanfang Ye, Chuxu Zhang

The effectiveness is further guaranteed and proved by the gradients' distance between the subset and the full set; (ii) empirically, we discover that during the learning process of a GNN, some samples in the training dataset are informative for providing gradients to update model parameters.

Contrastive Learning Graph Representation Learning

Contrastive Graph Few-Shot Learning

no code implementations30 Sep 2022 Chunhui Zhang, Hongfu Liu, Jundong Li, Yanfang Ye, Chuxu Zhang

Later, the trained encoder is frozen as a teacher model to distill a student model with a contrastive loss.

Contrastive Learning Few-Shot Learning +2

Graph Contrastive Learning with Cross-view Reconstruction

no code implementations16 Sep 2022 Qianlong Wen, Zhongyu Ouyang, Chunhui Zhang, Yiyue Qian, Yanfang Ye, Chuxu Zhang

In light of this, we introduce the Graph Contrastive Learning with Cross-View Reconstruction (GraphCV), which follows the information bottleneck principle to learn minimal yet sufficient representation from graph data.

Contrastive Learning Disentanglement +3

Few-Shot Learning on Graphs

no code implementations17 Mar 2022 Chuxu Zhang, Kaize Ding, Jundong Li, Xiangliang Zhang, Yanfang Ye, Nitesh V. Chawla, Huan Liu

In light of this, few-shot learning on graphs (FSLG), which combines the strengths of graph representation learning and few-shot learning together, has been proposed to tackle the performance degradation in face of limited annotated data challenge.

Few-Shot Learning Graph Mining +1

Disentangled Spatiotemporal Graph Generative Models

no code implementations28 Feb 2022 Yuanqi Du, Xiaojie Guo, Hengning Cao, Yanfang Ye, Liang Zhao

Spatiotemporal graph represents a crucial data structure where the nodes and edges are embedded in a geometric space and can evolve dynamically over time.

Disentanglement Graph Generation +1

Black-box Node Injection Attack for Graph Neural Networks

no code implementations18 Feb 2022 Mingxuan Ju, Yujie Fan, Yanfang Ye, Liang Zhao

Graph Neural Networks (GNNs) have drawn significant attentions over the years and been broadly applied to vital fields that require high security standard such as product recommendation and traffic forecasting.

Product Recommendation

Adaptive Kernel Graph Neural Network

1 code implementation8 Dec 2021 Mingxuan Ju, Shifu Hou, Yujie Fan, Jianan Zhao, Liang Zhao, Yanfang Ye

To solve this problem, in this paper, we propose a novel framework - i. e., namely Adaptive Kernel Graph Neural Network (AKGNN) - which learns to adapt to the optimal graph kernel in a unified manner at the first attempt.

Representation Learning

Distilling Meta Knowledge on Heterogeneous Graph for Illicit Drug Trafficker Detection on Social Media

1 code implementation NeurIPS 2021 Yiyue Qian, Yiming Zhang, Yanfang Ye, Chuxu Zhang

In this paper, we propose a holistic framework named MetaHG to automatically detect illicit drug traffickers on social media (i. e., Instagram), by tackling the following two new challenges: (1) different from existing works which merely focus on analyzing post content, MetaHG is capable of jointly modeling multi-modal content and relational structured information on social media for illicit drug trafficker detection; (2) in addition, through the proposed meta-learning technique, MetaHG addresses the issue of requiring sufficient data for model training.

Knowledge Distillation Marketing +3

Heterogeneous Temporal Graph Neural Network

1 code implementation26 Oct 2021 Yujie Fan, Mingxuan Ju, Chuxu Zhang, Liang Zhao, Yanfang Ye

To retain the heterogeneity, intra-relation aggregation is first performed over each slice of HTG to attentively aggregate information of neighbors with the same type of relation, and then intra-relation aggregation is exploited to gather information over different types of relations; to handle temporal dependencies, across-time aggregation is conducted to exchange information across different graph slices over the HTG.

Representation Learning

Gophormer: Ego-Graph Transformer for Node Classification

no code implementations25 Oct 2021 Jianan Zhao, Chaozhuo Li, Qianlong Wen, Yiqi Wang, Yuming Liu, Hao Sun, Xing Xie, Yanfang Ye

Existing graph transformer models typically adopt fully-connected attention mechanism on the whole input graph and thus suffer from severe scalability issues and are intractable to train in data insufficient cases.

Classification Data Augmentation +3

Knowledge-aware Coupled Graph Neural Network for Social Recommendation

1 code implementation8 Oct 2021 Chao Huang, Huance Xu, Yong Xu, Peng Dai, Lianghao Xia, Mengyin Lu, Liefeng Bo, Hao Xing, Xiaoping Lai, Yanfang Ye

While many recent efforts show the effectiveness of neural network-based social recommender systems, several important challenges have not been well addressed yet: (i) The majority of models only consider users' social connections, while ignoring the inter-dependent knowledge across items; (ii) Most of existing solutions are designed for singular type of user-item interactions, making them infeasible to capture the interaction heterogeneity; (iii) The dynamic nature of user-item interactions has been less explored in many social-aware recommendation techniques.

Collaborative Filtering Recommendation Systems

Detection of Illicit Drug Trafficking Events on Instagram: A Deep Multimodal Multilabel Learning Approach

no code implementations19 Aug 2021 Chuanbo Hu, Minglei Yin, Bin Liu, Xin Li, Yanfang Ye

Accordingly, accurate detection of illicit drug trafficking events (IDTEs) from social media has become even more challenging.

Marketing

Identifying Illicit Drug Dealers on Instagram with Large-scale Multimodal Data Fusion

no code implementations18 Aug 2021 Chuanbo Hu, Minglei Yin, Bin Liu, Xin Li, Yanfang Ye

Unlike existing methods that focus on posting-based detection, we propose to tackle the problem of illicit drug dealer identification by constructing a large-scale multimodal dataset named Identifying Drug Dealers on Instagram (IDDIG).

Community Detection

heterogeneous temporal graph transformer: an intelligent system for evolving android malware detection

1 code implementation KDD 2021 Yujie Fan, Mingxuan Ju, Shifu Hou, Yanfang Ye, Wenqiang Wan, Kui Wang, Yinming Mei, Qi Xiong

To capture malware evolution, we further consider the temporal dependence and introduce a heterogeneous temporal graph to jointly model malware propagation and evolution by considering heterogeneous spatial dependencies with temporal dimensions.

Android Malware Detection Malware Detection +2

A Survey on Heterogeneous Graph Embedding: Methods, Techniques, Applications and Sources

no code implementations30 Nov 2020 Xiao Wang, Deyu Bo, Chuan Shi, Shaohua Fan, Yanfang Ye, Philip S. Yu

Heterogeneous graphs (HGs) also known as heterogeneous information networks have become ubiquitous in real-world scenarios; therefore, HG embedding, which aims to learn representations in a lower-dimension space while preserving the heterogeneous structures and semantics for downstream tasks (e. g., node/graph classification, node clustering, link prediction), has drawn considerable attentions in recent years.

Graph Classification Graph Embedding +4

Interpretable Deep Graph Generation with Node-Edge Co-Disentanglement

1 code implementation9 Jun 2020 Xiaojie Guo, Liang Zhao, Zhao Qin, Lingfei Wu, Amarda Shehu, Yanfang Ye

Disentangled representation learning has recently attracted a significant amount of attention, particularly in the field of image representation learning.

Disentanglement Graph Generation

Arms Race in Adversarial Malware Detection: A Survey

no code implementations24 May 2020 Deqiang Li, Qianmu Li, Yanfang Ye, Shouhuai Xu

In this paper, we survey and systematize the field of Adversarial Malware Detection (AMD) through the lens of a unified conceptual framework of assumptions, attacks, defenses, and security properties.

Malware Detection

A Framework for Enhancing Deep Neural Networks Against Adversarial Malware

1 code implementation15 Apr 2020 Deqiang Li, Qianmu Li, Yanfang Ye, Shouhuai Xu

By conducting experiments with the Drebin Android malware dataset, we show that the framework can achieve a 98. 49\% accuracy (on average) against grey-box attacks, where the attacker knows some information about the defense and the defender knows some information about the attack, and an 89. 14% accuracy (on average) against the more capable white-box attacks, where the attacker knows everything about the defense and the defender knows some information about the attack.

General Classification Malware Detection

Hyperbolic Graph Attention Network

1 code implementation6 Dec 2019 Yiding Zhang, Xiao Wang, Xunqiang Jiang, Chuan Shi, Yanfang Ye

Graph neural network (GNN) has shown superior performance in dealing with graphs, which has attracted considerable research attention recently.

Anatomy Graph Attention

Temporal Network Embedding with Micro- and Macro-dynamics

1 code implementation10 Sep 2019 Yuanfu Lu, Xiao Wang, Chuan Shi, Philip S. Yu, Yanfang Ye

The micro-dynamics describe the formation process of network structures in a detailed manner, while the macro-dynamics refer to the evolution pattern of the network scale.

Network Embedding

Heterogeneous Graph Attention Network

2 code implementations WWW 2019 2019 Xiao Wang, Houye Ji, Chuan Shi, Bai Wang, Peng Cui, P. Yu, Yanfang Ye

With the learned importance from both node-level and semantic-level attention, the importance of node and meta-path can be fully considered.

Social and Information Networks

Enhancing Robustness of Deep Neural Networks Against Adversarial Malware Samples: Principles, Framework, and AICS'2019 Challenge

1 code implementation19 Dec 2018 Deqiang Li, Qianmu Li, Yanfang Ye, Shouhuai Xu

However, machine learning is known to be vulnerable to adversarial evasion attacks that manipulate a small number of features to make classifiers wrongly recognize a malware sample as a benign one.

Cryptography and Security 68-06

AiDroid: When Heterogeneous Information Network Marries Deep Neural Network for Real-time Android Malware Detection

no code implementations2 Nov 2018 Yanfang Ye, Shifu Hou, Lingwei Chen, Jingwei Lei, Wenqiang Wan, Jiabin Wang, Qi Xiong, Fudong Shao

In this paper, we first extract the runtime Application Programming Interface (API) call sequences from Android apps, and then analyze higher-level semantic relations within the ecosystem to comprehensively characterize the apps.

Android Malware Detection Malware Detection +1

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