OpenRaiser/Intern-Atlas huggingface dataset:
A possible simplification of the semantic web architecture
Authors: Bernardo Cuenca Grau
Venue: Conference WWW 2004
Year: 2004

In the semantic Web architecture, Web ontology languages arebuilt on top of RDF(S). However, serious difficulties have arisen when trying to layer expressive ontology languages, like OWL, on top of RDF-Schema. Although these problems can be avoided, OWL (andthe whole semantic Web architecture) becomes much more complex than it should be. In this paper, a possible simplification of thesemantic Web architecture is suggested, which has several import antadvantages with respect to the layering currently accepted by the W3C Ontology Working Group.
Generating Mobility Trajectories with Retained Data Utility
Authors: Chu Cao, Mo Li
Venue: Conference KDD 2021
Year: 2021

This paper presents TrajGen, an approach to generate artificial datasets of mobility trajectories based on an original trajectory dataset while retaining the utility of the original data in supporting various mobility applications. The generated mobility data is disentangled with the original data and can be shared without compromising the data privacy. TrajGen leverages Generative Adversarial Nets combined with a Seq2Seq model to generate the spatial-temporal trajectory data. TrajGen is implemented and evaluated with real-world taxi trajectory data in Singapore. The extensive experimental results demonstrate that TrajGen is able to generate artificial trajectory data that retain key statistical characteristics of the original data. Two case studies, i.e. road map updating and Origin-Destination demand estimation are performed with the generated artificial data, and the results show that the artificial trajectories generated by TrajGen retain the utility of original data in supporting the two applications.
A graph-based approach for trajectory similarity computation in spatial networks
Authors: Peng Han, Jin Wang, Di Yao, Shuo Shang, Xiangliang Zhang
Venue: Conference KDD 2021
Year: 2021

Trajectory similarity computation is an essential operation in many applications of spatial data analysis. In this paper, we study the problem of trajectory similarity computation over spatial network, where the real distances between objects are reflected by the network distance. Unlike previous studies which learn the representation of trajectories in Euclidean space, it requires to capture not only the sequence information of the trajectory but also the structure of spatial network. To this end, we propose GTS, a brand new framework that can jointly learn both factors so as to accurately compute the similarity. It first learns the representation of each point-of-interest (POI) in the road network along with the trajectory information. This is realized by incorporating the distances between POIs and trajectory in the random walk over the spatial network as well as the loss function. Then the trajectory representation is learned by a Graph Neural Network model to identify neighboring POIs within the same trajectory, together with an LSTM model to capture the sequence information in the trajectory. We conduct comprehensive evaluation on several real world datasets. The experimental results demonstrate that our model substantially outperforms all existing approaches.
Gromov-wasserstein multi-modal alignment and clustering
Authors: Fengjiao Gong, Yuzhou Nie, Hongteng Xu
Venue: Conference CIKM 2022
Year: 2022

Multi-modal clustering aims at finding a clustering structure shared by the data of different modalities in an unsupervised way. Currently, solving this problem often relies on two assumptions: i) the multi-modal data own the same latent distribution, and ii) the observed multi-modal data are well-aligned and without any missing modalities. Unfortunately, these two assumptions are often questionable in practice and thus limit the feasibility of many multi-modal clustering methods. In this work, we develop a new multi-modal clustering method based on the Gromovization of optimal transport distance, which relaxes the dependence on the above two assumptions. In particular, given the data of different modalities, whose correspondence is unknown, our method learns the Gromov-Wasserstein (GW) barycenter of their kernel matrices. Driven by the modularity maximization principle, the GW barycenter helps to explore the clustering structure shared by different modalities. Moreover, the GW barycenter is associated with the GW distances between the different modalities to the clusters, and the optimal transport plans corresponding to the GW distances help to achieve the alignment and the clustering of the multi-modal data jointly. Experimental results show that our method outperforms state-of-the-art multi-modal clustering methods, especially when the data are (partially or completely) unaligned. The code is available at https://github.com/rucnyz/GWMAC.
Retrieval-augmented hypergraph for multimodal social media popularity prediction
Authors: Zhangtao Cheng, Jienan Zhang, Xovee Xu, Goce Trajcevski, Ting Zhong, Fan Zhou 0002
Venue: Conference KDD 2024
Year: 2024

Accurately predicting the popularity of multimodal user-generated content (UGC) is fundamental for many real-world applications such as online advertising and recommendation. Existing approaches generally focus on limited contextual information within individual UGCs, yet overlook the potential benefit of exploiting meaningful knowledge in relevant UGCs. In this work, we propose RAGTrans, an aspect-aware retrieval-augmented multi-modal hypergraph transformer that retrieves pertinent knowledge from a multi-modal memory bank and enhances UGC representations via neighborhood knowledge aggregation on multi-model hypergraphs. In particular, we initially retrieve relevant multimedia instances from a large corpus of UGCs via the aspect information and construct a knowledge-enhanced hypergraph based on retrieved relevant instances. This allows capturing meaningful contextual information across the data. We then design a novel bootstrapping hypergraph transformer on multimodal hypergraphs to strengthen UGC representations across modalities via customizing a propagation algorithm to effectively diffuse information across nodes and edges. Additionally, we propose a user-aware attention-based fusion module to comprise the enriched UGC representations for popularity prediction. Extensive experiments on real-world social media datasets demonstrate that RAGTrans outperforms state-of-the-art popularity prediction models across settings.
Graph structural attack by perturbing spectral distance
Authors: Lu Lin 0001, Ethan Blaser, Hongning Wang
Venue: Conference KDD 2022
Year: 2022

Graph Convolutional Networks (GCNs) have fueled a surge of research interest due to their encouraging performance on graph learning tasks, but they are also shown vulnerability to adversarial attacks. In this paper, an effective graph structural attack is investigated to disrupt graph spectral filters in the Fourier domain, which are the theoretical foundation of GCNs. We define the notion of spectral distance based on the eigenvalues of graph Laplacian to measure the disruption of spectral filters. We realize the attack by maximizing the spectral distance and propose an efficient approximation to reduce the time complexity brought by eigen-decomposition. The experiments demonstrate the remarkable effectiveness of the proposed attack in both black-box and white-box settings for both test-time evasion attacks and training-time poisoning attacks. Our qualitative analysis suggests the connection between the imposed spectral changes in the Fourier domain and the attack behavior in the spatial domain, which provides empirical evidence that maximizing spectral distance is an effective way to change the graph structural property and thus disturb the frequency components for graph filters to affect the learning of GCNs.
Pick and choose: a GNN-based imbalanced learning approach for fraud detection
Authors: Yang Liu, Xiang Ao, Zidi Qin, Jianfeng Chi, Jinghua Feng, Hao Yang, Qing He
Venue: Conference WWW 2021
Year: 2021

Graph-based fraud detection approaches have escalated lots of attention recently due to the abundant relational information of graph-structured data, which may be beneficial for the detection of fraudsters. However, the GNN-based algorithms could fare poorly when the label distribution of nodes is heavily skewed, and it is common in sensitive areas such as financial fraud, etc. To remedy the class imbalance problem of graph-based fraud detection, we propose a Pick and Choose Graph Neural Network (PC-GNN for short) for imbalanced supervised learning on graphs. First, nodes and edges are picked with a devised label-balanced sampler to construct sub-graphs for mini-batch training. Next, for each node in the sub-graph, the neighbor candidates are chosen by a proposed neighborhood sampler. Finally, information from the selected neighbors and different relations are aggregated to obtain the final representation of a target node. Experiments on both benchmark and real-world graph-based fraud detection tasks demonstrate that PC-GNN apparently outperforms state-of-the-art baselines.
H2-fdetector: A gnn-based fraud detector with homophilic and heterophilic connections
Authors: Fengzhao Shi, Yanan Cao, Yanmin Shang, Yuchen Zhou, Chuan Zhou, Jia Wu
Venue: Conference WWW 2022
Year: 2022

In the fraud graph, fraudsters often interact with a large number of benign entities to hide themselves. So, there are not only the homophilic connections formed by the same label nodes (similar nodes), but also the heterophilic connections formed by the different label nodes (dissimilar nodes). However, the existing GNN-based fraud detection methods just enhance the homophily in fraud graph and use the low-pass filter to retain the commonality of node features among the neighbors, which inevitably ignore the difference among neighbor of heterophilic connections. To address this problem, we propose a Graph Neural Network-based Fraud Detector with Homophilic and Heterophilic Interactions (H2-FDetector for short). Firstly, we identify the homophilic and heterophilic connections with the supervision of labeled nodes. Next, we design a new information aggregation strategy to make the homophilic connections propagate similar information and the heterophilic connections propagate difference information. Finally, a prototype prior is introduced to guide the identification of fraudsters. Extensive experiments on two real public benchmark fraud detection tasks demonstrate that our method apparently outperforms state-of-the-art baselines.
Attacking fake news detectors via manipulating news social engagement
Authors: Haoran Wang, Yingtong Dou, Canyu Chen, Lichao Sun, Philip S. Yu, Kai Shu
Venue: Conference WWW 2023
Year: 2023

Social media is one of the main sources for news consumption, especially among the younger generation. With the increasing popularity of news consumption on various social media platforms, there has been a surge of misinformation which includes false information or unfounded claims. As various text- and social context-based fake news detectors are proposed to detect misinformation on social media, recent works start to focus on the vulnerabilities of fake news detectors. In this paper, we present the first adversarial attack framework against Graph Neural Network (GNN)-based fake news detectors to probe their robustness. Specifically, we leverage a multi-agent reinforcement learning (MARL) framework to simulate the adversarial behavior of fraudsters on social media. Research has shown that in real-world settings, fraudsters coordinate with each other to share different news in order to evade the detection of fake news detectors. Therefore, we modeled our MARL framework as a Markov Game with bot, cyborg, and crowd worker agents, which have their own distinctive cost, budget, and influence. We then use deep Q-learning to search for the optimal policy that maximizes the rewards. Extensive experimental results on two real-world fake news propagation datasets demonstrate that our proposed framework can effectively sabotage the GNN-based fake news detector performance. We hope this paper can provide insights for future research on fake news detection.
Precision cityshield against hazardous chemicals threats via location mining and self-supervised learning
Authors: Jiahao Ji, Jingyuan Wang 0001, Junjie Wu 0002, Boyang Han, Junbo Zhang 0004, Yu Zheng 0004
Venue: Conference KDD 2022
Year: 2022

With the unprecedented development of industrialization and urbanization, many hazardous chemicals have become an indispensable part of our daily life. They are produced, transported, and consumed in modern cities every day, which breeds many unknown hazardous chemicals-related locations (HCLs) that are out of the supervision of management departments and accompanying huge threats to urban safety. How to recognize these unknown HCLs and identify their risk levels is an essential task for urban hazardous chemicals management. To accomplish this task, in this work, we propose a system named as CityShield to discover hidden HCLs and classify their risk levels based on trajectories of hazardous chemicals transportation vehicles. The CityShield system consists of three components. The first component is Data Pre-processing, which filters noises in raw trajectories and probes stable transportation vehicles' stay points from massive uncertain GPS points. The second is HCL Recognition, which adopts the proposed HCL-Rec algorithm to cluster stay points into polygonal HCLs, and avoids the improper location merging problem caused by the skewed spatial distribution of HCLs. The third component is HCL Classification, which introduces the HCL relation graph as auxiliary information to overcome the label scarcity problem of HCLs. It adopts a self-supervised method consisting of four pre-training tasks to learn high-quality representations for HCLs from the graph, which are finally used to classify the categories and risk levels of HCLs.
Urban region representation learning with openstreetmap building footprints
Authors: Yi Li 0044, Weiming Huang 0001, Gao Cong, Hao Wang 0068, Zheng Wang 0046
Venue: Conference KDD 2023
Year: 2023

The prosperity of crowdsourcing geospatial data provides increasing opportunities to understand our cities. In particular, OpenStreetMap (OSM) has become a prominent vault of geospatial data on the Web. In this context, learning urban region representations from OSM data, which is unexplored in previous work, could be profitable for various downstream tasks. In this work, we utilize OSM buildings (footprints) complemented with points of interest (POIs) to learn region representations, as buildings' shapes, spatial distributions, and properties have tight linkages to different urban functions. However, appealing as it seems, urban buildings often exhibit complex patterns to form dense or sparse areas, which brings significant challenges for unsupervised feature extraction. To address the challenges, we propose RegionDCL1, an unsupervised framework to deeply mine urban buildings. In a nutshell, we leverage random points generated by Poisson Disk Sampling to tackle data-sparse areas and utilize triplet loss with a novel adaptive margin to preserve inter-region correlations. Furthermore, we train our model with group-level and region-level contrastive learning, making it adaptive to varying region partitions. Extensive experiments in two global cities demonstrate that RegionDCL consistently outperforms the state-of-the-art counterparts across different region partitions, and outputs effective representations for inferring urban land use and population density.
Learning to effectively estimate the travel time for fastest route recommendation
Authors: Ning Wu, Jingyuan Wang, Wayne Xin Zhao, Yang Jin
Venue: Conference CIKM 2019
Year: 2019

Fastest Route Recommendation (FRR) aims to find the fastest path in response to user's queries in a large complex road network. Early studies cast the FRR task as a pathfinding problem on graphs and adopt heuristic algorithms as the major solution due to the efficiency and robustness. A major problem of heuristic algorithms is that the heuristic function is usually empirically set with simple methods, which is difficult to model other useful factors. In this paper, we extend the classic A* algorithm for the FRR task by modeling complex traffic information with neural networks. Specially, we identify an important factor that is important to improve the FRR task, i.e. the estimation of travel time. For this purpose, we first develop a module for predicting the time-varying traffic speed for a road segment, which is the foundation for estimating the travel time. Conditioned on this module, we further design another module to estimate the fastest travel time between two locations connected by routes. We adopt neural networks to implement both modules for enabling the capacity of modeling complex traffic characteristics and dynamics. In this way, the original two cost functions of A* algorithm have been set in a more principled way with neural networks. To our knowledge, we are the first to use neural networks for improving A* algorithm in the FRR task. It elegantly combines the merits of A* algorithm and the powerful modeling capacities of neural networks for the FRR task. Extensive results on the three real-world datasets have shown the effectiveness and robustness of the proposed model.
Urbanclip: Learning text-enhanced urban region profiling with contrastive language-image pretraining from the web
Authors: Yibo Yan, Haomin Wen, Siru Zhong, Wei Chen, Haodong Chen, Qingsong Wen, Roger Zimmermann, Yuxuan Liang
Venue: Conference WWW 2024
Year: 2024

Urban region profiling from web-sourced data is of utmost importance for urban planning and sustainable development. We are witnessing a rising trend of LLMs for various fields, especially dealing with multi-modal data research such as vision-language learning, where the text modality serves as a supplement information for the image. Since textual modality has never been introduced into modality combinations in urban region profiling, we aim to answer two fundamental questions in this paper: i) Can textual modality enhance urban region profiling? ii) and if so, in what ways and with regard to which aspects? To answer the questions, we leverage the power of Large Language Models (LLMs) and introduce the first-ever LLM-enhanced framework that integrates the knowledge of textual modality into urban imagery profiling, named LLM-enhanced Urban Region Profiling with Contrastive Language-Image Pretraining (UrbanCLIP). Specifically, it first generates a detailed textual description for each satellite image by an open-source Image-to-Text LLM. Then, the model is trained on the image-text pairs, seamlessly unifying natural language supervision for urban visual representation learning, jointly with contrastive loss and language modeling loss. Results on predicting three urban indicators in four major Chinese metropolises demonstrate its superior performance, with an average improvement of 6.1% on R^2 compared to the state-of-the-art methods. Our code and the image-language dataset will be released upon paper notification.
A relevant and diverse retrieval-enhanced data augmentation framework for sequential recommendation
Authors: Shuqing Bian, Wayne Xin Zhao, Jinpeng Wang, Ji-Rong Wen
Venue: Conference CIKM 2022
Year: 2022

Within online platforms, it is critical to capture the semantics of sequential user behaviors for accurately predicting user interests. Recently, significant progress has been made in sequential recommendation with deep learning. However, existing neural sequential recommendation models may not perform well in practice due to the sparsity of the real-world data especially in cold-start scenarios. To tackle this problem, we propose the model ReDA, which stands for Retrieval-enhanced Data Augmentation for modeling sequential user behaviors. The main idea of our approach is to leverage the related information from similar users for generating both relevant and diverse augmentation. First, we train a neural retriever to retrieve the augmentation users according to the se- mantic similarity between user representations, and then conduct two types of data augmentation to generate augmented user representations. Furthermore, these augmented data are incorporated in a contrastive learning framework for learning more capable representations. Extensive experiments conducted on both public and industry datasets demonstrate the superiority of our proposed method over existing state-of-the-art methods, especially when only limited training data is available.
Diffusion augmentation for sequential recommendation
Authors: Qidong Liu, Fan Yan, Xiangyu Zhao, Zhaocheng Du, Huifeng Guo, Ruiming Tang, Feng Tian
Venue: Conference CIKM 2023
Year: 2023

Sequential recommendation (SRS) has become the technical foundation in many applications recently, which aims to recommend the next item based on the user's historical interactions. However, sequential recommendation often faces the problem of data sparsity, which widely exists in recommender systems. Besides, most users only interact with a few items, but existing SRS models often underperform these users. Such a problem, named the long-tail user problem, is still to be resolved. Data augmentation is a distinct way to alleviate these two problems, but they often need fabricated training strategies or are hindered by poor-quality generated interactions. To address these problems, we propose a Diffusion Augmentation for Sequential Recommendation (DiffuASR) for a higher quality generation. The augmented dataset by DiffuASR can be used to train the sequential recommendation models directly, free from complex training procedures. To make the best of the generation ability of the diffusion model, we first propose a diffusion-based pseudo sequence generation framework to fill the gap between image and sequence generation. Then, a sequential U-Net is designed to adapt the diffusion noise prediction model U-Net to the discrete sequence generation task. At last, we develop two guide strategies to assimilate the preference between generated and origin sequences. To validate the proposed DiffuASR, we conduct extensive experiments on three real-world datasets with three sequential recommendation models. The experimental results illustrate the effectiveness of DiffuASR. As far as we know, DiffuASR is one pioneer that introduce the diffusion model to the recommendation.The implementation code is available online.
Learning to Augment for Casual User Recommendation
Authors: Jianling Wang, Ya Le, Bo Chang, Yuyan Wang, Ed H. Chi, Minmin Chen
Venue: Conference WWW 2022
Year: 2022

Users who come to recommendation platforms are heterogeneous in activity levels. There usually exists a group of core users who visit the platform regularly and consume a large body of content upon each visit, while others are casual users who tend to visit the platform occasionally and consume less each time. As a result, consumption activities from core users often dominate the training data used for learning. As core users can exhibit different activity patterns from casual users, recommender systems trained on historical user activity data usually achieve much worse performance on casual users than core users. To bridge the gap, we propose a model-agnostic framework L2Aug to improve recommendations for casual users through data augmentation, without sacrificing core user experience. L2Aug is powered by a data augmentor that learns to generate augmented interaction sequences, in order to fine-tune and optimize the performance of the recommendation system for casual users. On four real-world public datasets, L2Aug outperforms other treatment methods and achieves the best sequential recommendation performance for both casual and core users. We also test L2Aug in an online simulation environment with real-time feedback to further validate its efficacy, and showcase its flexibility in supporting different augmentation actions.