OpenRaiser/Intern-Atlas huggingface dataset:
BLADE: Biased Neighborhood Sampling based Graph Neural Network for Directed Graphs
Authors: Srinivas Virinchi, Anoop Saladi
Venue: WSDM
Year: 2023

Directed graphs are ubiquitous and have applications across multiple domains including citation, website, social, and traffic networks. Yet, majority of research involving graph neural networks (GNNs) focus on undirected graphs. In this paper, we deal with the problem of node recommendation in directed graphs. Specifically, given a directed graph and query node as input, the goal is to recommend top- nodes that have a high likelihood of a link with the query node. Here we propose BLADE, a novel GNN to model directed graphs. In order to jointly capture link likelihood and link direction, we employ an asymmetric loss function and learn dual embeddings for each node, by appropriately aggregating features from its neighborhood. In order to achieve optimal performance on both low and high-degree nodes, we employ a biased neighborhood sampling scheme that generates locally varying neighborhoods which differ based on a node's connectivity structure. Extensive experimentation on several open-source and proprietary directed graphs show that BLADE outperforms state-of-the-art baselines by 6-230% in terms of HitRate and MRR for the node recommendation task and 10.5% in terms of AUC for the link direction prediction task. We perform ablation study to accentuate the importance of biased neighborhood sampling employed in generating higher quality recommendations for both low-degree and high-degree query nodes. Further, BLADE delivers significant improvement in revenue and sales as measured through an A/B experiment.
Effective Graph Kernels for Evolving Functional Brain Networks
Authors: Xinlei Wang, Jinyi Chen, Bing Tian Dai, Junchang Xin, Yu Gu, Ge Yu
Venue: WSDM
Year: 2023

The graph kernel of the functional brain network is an effective method in the field of neuropsychiatric disease diagnosis like Alzheimer's Disease (AD). The traditional static brain networks cannot reflect dynamic changes of brain activities, but evolving brain networks, which are a series of brain networks over time, are able to seize such dynamic changes. As far as we know, the graph kernel method is effective for calculating the differences among networks. Therefore, it has a great potential to understand the dynamic changes of evolving brain networks, which are a series of chronological differences. However, if the conventional graph kernel methods which are built for static networks are applied directly to evolving networks, the evolving information will be lost and accurate diagnostic results will be far from reach. We propose an effective method, called Global Matching based Graph Kernels (GM-GK), which captures dynamic changes of evolving brain networks and significantly improves classification accuracy. At the same time, in order to reflect the natural properties of the brain activity of the evolving brain network neglected by the GM-GK method, we also propose a Local Matching based Graph Kernel (LM-GK), which allows the order of the evolving brain network to be locally fine-tuned. Finally, the experiments are conducted on real data sets and the results show that the proposed methods can significantly improve the neuropsychiatric disease diagnostic accuracy.
A Multi-graph Fusion Based Spatiotemporal Dynamic Learning Framework
Authors: Xu Wang, Lianliang Chen, Hongbo Zhang, Pengkun Wang, Zhengyang Zhou, Yang Wang
Venue: WSDM
Year: 2023

Spatiotemporal data forecasting is a fundamental task in the field of graph data mining. Typical spatiotemporal data prediction methods usually capture spatial dependencies by directly aggregating features of local neighboring vertices in a fixed graph. However, this kind of aggregators can only capture localized correlations between vertices, and while been stacked for larger receptive field, they fall into the dilemma of over-smoothing. Additional, in temporal perspective, traditional methods focus on fixed graphs, while the correlations among vertexes can be dynamic. And time series components integrated strategies in traditional spatiotemporal learning methods can hardly handle frequently and drastically changed sequences. To overcome those limitations of existing works, in this paper, we propose a novel multi-graph based dynamic learning framework. First, a novel Dynamic Neighbor Search (DNS) mechanism is introduced to model global dynamic correlations between vertices by constructing a feature graph (FG), where the adjacency matrix is dynamically determined by DNS. Then we further alleviate the over-smoothing issue with our newly designed Adaptive Heterogeneous Representation (AHR) module. Both FG and origin graph (OG) are fed into the AHR modules and fused in our proposed Multi-graph Fusion block. Additionally, we design a Differential Vertex Representation (DVR) module which takes advantage of differential information to model temporal trends. Extensive experiments illustrate the superior forecasting performances of our proposed multi-graph based dynamic learning framework on six real-world spatiotemporal datasets from different cities and domains, and this corroborates the solid effectiveness of our proposed framework and its superior generalization ability.
Heterogeneous Graph-based Context-aware Document Ranking
Authors: Shuting Wang, Zhicheng Dou, Yutao Zhu
Venue: WSDM
Year: 2023

Users' complex information needs usually require consecutive queries, which results in sessions with a series of interactions. Exploiting such contextual interactions has been proven to be favorable for result ranking. However, existing studies mainly model the contextual information independently and sequentially. They neglect the diverse information hidden in different relations and structured information of session elements as well as the valuable signals from other relevant sessions. In this paper, we propose HEXA, a heterogeneous graph-based context-aware document ranking framework. It exploits heterogeneous graphs to organize the contextual information and beneficial search logs for modeling user intents and ranking results. Specifically, we construct two heterogeneous graphs, i.e., a session graph and a query graph. The session graph is built from the current session queries and documents. Meanwhile, we sample the current query's k-layer neighbors from search logs to construct the query graph. Then, we employ heterogeneous graph neural networks and specialized readout functions on the two graphs to capture the user intents from local and global aspects. Finally, the document ranking scores are measured by how well the documents are matched with the two user intents. Results on two large-scale datasets confirm the effectiveness of our model.
Graph Explicit Neural Networks: Explicitly Encoding Graphs for Efficient and Accurate Inference
Authors: Yiwei Wang, Bryan Hooi, Yozen Liu, Neil Shah
Venue: WSDM
Year: 2023

As the state-of-the-art graph learning models, the message passing based neural networks (MPNNs) implicitly use the graph topology as the "pathways" to propagate node features. This implicit use of graph topology induces the MPNNs' over-reliance on (node) features and high inference latency, which hinders their large-scale applications in industrial contexts. To mitigate these weaknesses, we propose the Graph Explicit Neural Network (GENN) framework. GENN can be flexibly applied to various MPNNs and improves them by providing more efficient and accurate inference that is robust in feature-constrained settings. Specifically, we carefully incorporate recent developments in network embedding methods to efficiently prioritize the graph topology for inference. From this vantage, GENN explicitly encodes the topology as an important source of information to mitigate the reliance on node features. Moreover, by adopting knowledge distillation (KD) techniques, GENN takes an MPNN as the teacher to supervise the training for better effectiveness while avoiding the teacher's high inference latency. Empirical results show that our GENN infers dramatically faster than its MPNN teacher by 40x-78x. In terms of accuracy, GENN yields significant gains (more than 40%) for its MPNN teacher when the node features are limited based on our explicit encoding. Moreover, GENN outperforms the MPNN teacher even in feature-rich settings thanks to our KD design.
Improving News Recommendation with Channel-Wise Dynamic Representations and Contrastive User Modeling
Authors: Jingkun Wang, Yongtao Jiang, Haochen Li, Wen Zhao
Venue: WSDM
Year: 2023

News modeling and user modeling are the two core tasks of news recommendation. Accurate user representation and news representation can enable the recommendation system to provide users with precise recommendation services. Most existing methods use deep learning models such as CNN and Self-Attention to extract text features from news titles and abstracts to generate specific news vectors. However, the CNN-based methods have fixed parameters and cannot extract specific features for different input words, while the Self-Attention-based methods have high computational costs and are difficult to capture local features effectively. In our proposed method, we build a category-based dynamic component to generate suitable parameters for different inputs and extract local features from multiple perspectives. Meanwhile, users will mistakenly click on some news terms they are not interested in, so there will be some interaction noises in the datasets. In order to explore the critical user behaviors in user data and reduce the impact of noise data on user modeling, we adopt a frequency-aware contrastive learning method in user modeling. Experiments on real-world datasets verify the effectiveness of our proposed method.
AGREE: Aligning Cross-Modal Entities for Image-Text Retrieval Upon Vision-Language Pre-trained Models
Authors: Xiaodan Wang, Lei Li, Zhixu Li, Xuwu Wang, Xiangru Zhu, Chengyu Wang, Jun Huang, Yanghua Xiao
Venue: WSDM
Year: 2023

Image-text retrieval is a challenging cross-modal task that arouses much attention. While the traditional methods cannot break down the barriers between different modalities, Vision-Language Pre-trained (VLP) models greatly improve image-text retrieval performance based on massive image-text pairs. Nonetheless, the VLP-based methods are still prone to produce retrieval results that cannot be cross-modal aligned with entities. Recent efforts try to fix this problem at the pre-training stage, which is not only expensive but also unpractical due to the unavailable of full datasets. In this paper, we novelly propose a lightweight and practical approach to align cross-modal entities for image-text retrieval upon VLP models only at the fine-tuning and re-ranking stages. We employ external knowledge and tools to construct extra fine-grained image-text pairs, and then emphasize cross-modal entity alignment through contrastive learning and entity-level mask modeling in fine-tuning. Besides, two re-ranking strategies are proposed, including one specially designed for zero-shot scenarios. Extensive experiments with several VLP models on multiple Chinese and English datasets show that our approach achieves state-of-the-art results in nearly all settings.
Knowledge-Adaptive Contrastive Learning for Recommendation
Authors: Hao Wang, Yao Xu, Cheng Yang, Chuan Shi, Xin Li, Ning Guo, Zhiyuan Liu
Venue: WSDM
Year: 2023

By jointly modeling user-item interactions and knowledge graph (KG) information, KG-based recommender systems have shown their superiority in alleviating data sparsity and cold start problems. Recently, graph neural networks (GNNs) have been widely used in KG-based recommendation, owing to the strong ability of capturing high-order structural information. However, we argue that existing GNN-based methods have the following two limitations. Interaction domination: the supervision signal of user-item interaction will dominate the model training, and thus the information of KG is barely encoded in learned item representations; Knowledge overload: KG contains much recommendation-irrelevant information, and such noise would be enlarged during the message aggregation of GNNs. The above limitations prevent existing methods to fully utilize the valuable information lying in KG. In this paper, we propose a novel algorithm named Knowledge-Adaptive Contrastive Learning (KACL) to address these challenges. Specifically, we first generate data augmentations from user-item interaction view and KG view separately, and perform contrastive learning across the two views. Our design of contrastive loss will force the item representations to encode information shared by both views, thereby alleviating the interaction domination issue. Moreover, we introduce two learnable view generators to adaptively remove task-irrelevant edges during data augmentation, and help tolerate the noises brought by knowledge overload. Experimental results on three public benchmarks demonstrate that KACL can significantly improve the performance on top-K recommendation compared with state-of-the-art methods.
AntTS: A Toolkit for Time Series Forecasting in Industrial Scenarios
Authors: Jianping Wei, Zhibo Zhu, Ziqi Liu, Zhiqiang Zhang, Jun Zhou
Venue: WSDM
Year: 2023

Time series forecasting is an important ingredient in the intelligence of business and decision processes. In industrial scenarios, the time series of interest are mostly macroscopic time series that are aggregated from microscopic time series, e.g., the retail sales is aggregated from the sales of different goods, and that are also intervened by certain treatments on the microscopic individuals, e.g., issuing discount coupons on some goods to increase the retail sales. These characteristics are not considered in existing toolkits, which just focus on the "natural" time series forecasting that predicts the future value based on historical data, regardless of the impact of treatments. In this paper, we present AntTS, a time series toolkit paying more attention on the forecasting of the macroscopic time series with underlying microscopic time series and certain treatments, besides the "natural" time series forecasting. AntTS consists of three decoupled modules, namely Clustering module, Natural Forecasting module, and Effect module, which are utilized to study the homogeneous groups of microscopic individuals, the "natural" time series forecasting of homogeneous groups, and the treatment effect estimation of homogeneous groups. With the combinations of different modules, it can exploit the microscopic individuals and the interventions on them, to help the forecasting of macroscopic time series. We show that AntTS helps address many typical tasks in the industry.
Generalizing Graph Neural Network across Graphs and Time
Authors: Zhihao Wen
Venue: WSDM
Year: 2023

Graph-structured data widely exist in diverse real-world scenarios, analysis of these graphs can uncover valuable insights about their respective application domains. However, most previous works focused on learning node representation from a single fixed graph, while many real-world scenarios require representations to be quickly generated for unseen nodes, new edges, or entirely new graphs. This inductive ability is essential for high-throughtput machine learning systems. However, this inductive graph representation problem is quite difficult, compared to the transductive setting, for that generalizing to unseen nodes requires new subgraphs containing the new nodes to be aligned to the neural network trained already. Meanwhile, following a message passing framework, graphneural network (GNN) is an inductive and powerful graph representation tool. We further explore inductive GNN from more specific perspectives: (1) generalizing GNN across graphs, in which we tackle with the problem of semi-supervised node classification across graphs; (2) generalizing GNN across time, in which we mainly solve the problem of temporal link prediction; (3) generalizing GNN across tasks; (4) generalizing GNN across locations.
Modeling Fine-grained Information via Knowledge-aware Hierarchical Graph for Zero-shot Entity Retrieval
Authors: Taiqiang Wu, Xingyu Bai, Weigang Guo, Weijie Liu, Siheng Li, Yujiu Yang
Venue: WSDM
Year: 2023

Zero-shot entity retrieval, aiming to link mentions to candidate entities under the zero-shot setting, is vital for many tasks in Natural Language Processing. Most existing methods represent mentions/entities via the sentence embeddings of corresponding context from the Pre-trained Language Model. However, we argue that such coarse-grained sentence embeddings can not fully model the mentions/entities, especially when the attention scores towards mentions/entities are relatively low. In this work, we propose GER, a Graph enhanced Entity Retrieval framework, to capture more fine-grained information as complementary to sentence embeddings. We extract the knowledge units from the corresponding context and then construct a mention/entity centralized graph. Hence, we can learn the fine-grained information about mention/entity by aggregating information from these knowledge units. To avoid the graph bottleneck for the central mention/entity node, we construct a hierarchical graph and design a novel Hierarchical Graph Attention Network~(HGAN). Experimental results on popular benchmarks demonstrate that our proposed GER framework performs better than previous state-of-the-art models.
A Bird's-eye View of Reranking: From List Level to Page Level
Authors: Yunjia Xi, Jianghao Lin, Weiwen Liu, Xinyi Dai, Weinan Zhang, Rui Zhang, Ruiming Tang, Yong Yu
Venue: WSDM
Year: 2023

Reranking, as the final stage of multi-stage recommender systems, refines the initial lists to maximize the total utility. With the development of multimedia and user interface design, the recommendation page has evolved to a multi-list style. Separately employing traditional list-level reranking methods for different lists overlooks the inter-list interactions and the effect of different page formats, thus yielding suboptimal reranking performance. Moreover, simply applying a shared network for all the lists fails to capture the commonalities and distinctions in user behaviors on different lists. To this end, we propose to draw a bird's-eye view of page-level reranking and design a novel Page-level Attentional Reranking (PAR) model. We introduce a hierarchical dual-side attention module to extract personalized intra- and inter-list interactions. A spatial-scaled attention network is devised to integrate the spatial relationship into pairwise item influences, which explicitly models the page format. The multi-gated mixture-of-experts module is further applied to capture the commonalities and differences of user behaviors between different lists. Extensive experiments on a public dataset and a proprietary dataset show that PAR significantly outperforms existing baseline models.
Making Pre-trained Language Models End-to-end Few-shot Learners with Contrastive Prompt Tuning
Authors: Ziyun Xu, Chengyu Wang, Minghui Qiu, Fuli Luo, Runxin Xu, Songfang Huang, Jun Huang
Venue: WSDM
Year: 2023

Pre-trained Language Models (PLMs) have achieved remarkable performance for various language understanding tasks in IR systems, which require the fine-tuning process based on labeled training data. For low-resource scenarios, prompt-based learning for PLMs exploits prompts as task guidance and turns downstream tasks into masked language problems for effective few-shot fine-tuning. In most existing approaches, the high performance of prompt-based learning heavily relies on handcrafted prompts and verbalizers, which may limit the application of such approaches in real-world scenarios. To solve this issue, we present CP-Tuning, an end-to-end Contrastive Prompt Tuning framework for fine-tuning PLMs without any manual engineering of task-specific prompts and verbalizers. It is integrated with the task-invariant continuous prompt encoding technique with fully trainable prompt parameters. We further propose the pair-wise cost-sensitive contrastive learning procedure to optimize the model in order to achieve verbalizer-free class mapping and enhance the task-invariance of prompts. It explicitly learns to distinguish different classes and makes the decision boundary smoother by assigning different costs to easy and hard cases. Experiments over a variety of language understanding tasks and different PLMs show that CP-Tuning outperforms state-of-the-art methods.
Self-supervised Graph Representation Learning for Black Market Account Detection
Authors: Zequan Xu, Lianyun Li, Hui Li, Qihang Sun, Shaofeng Hu, Rongrong Ji
Venue: WSDM
Year: 2023

Nowadays, Multi-purpose Messaging Mobile App (MMMA) has become increasingly prevalent. MMMAs attract fraudsters and some cybercriminals provide support for frauds via black market accounts (BMAs). Compared to fraudsters, BMAs are not directly involved in frauds and are more difficult to detect. This paper illustrates our BMA detection system SGRL (Self-supervised Graph Representation Learning) used in WeChat, a representative MMMA with over a billion users. We tailor Graph Neural Network and Graph Self-supervised Learning in SGRL for BMA detection. The workflow of SGRL contains a pretraining phase that utilizes structural information, node attribute information and available human knowledge, and a lightweight detection phase. In offline experiments, SGRL outperforms state-of-the-art methods by 16.06%-58.17% on offline evaluation measures. We deploy SGRL in the online environment to detect BMAs on the billion-scale WeChat graph, and it exceeds the alternative by 7.27% on the online evaluation measure. In conclusion, SGRL can alleviate label reliance, generalize well to unseen data, and effectively detect BMAs in WeChat.
Hansel: A Chinese Few-Shot and Zero-Shot Entity Linking Benchmark
Authors: Zhenran Xu, Zifei Shan, Yuxin Li, Baotian Hu, Bing Qin
Venue: WSDM
Year: 2023

Modern Entity Linking (EL) systems entrench a popularity bias, yet there is no dataset focusing on tail and emerging entities in languages other than English. We present Hansel, a new benchmark in Chinese that fills the vacancy of non-English few-shot and zero-shot EL challenges. The test set of Hansel is human annotated and reviewed, created with a novel method for collecting zero-shot EL datasets. It covers 10K diverse documents in news, social media posts and other web articles, with Wikidata as its target Knowledge Base. We demonstrate that the existing state-of-the-art EL system performs poorly on Hansel (R@1 of 36.6% on Few-Shot). We then establish a strong baseline that scores a R@1 of 46.2% on Few-Shot and 76.6% on Zero-Shot on our dataset. We also show that our baseline achieves competitive results on TAC-KBP2015 Chinese Entity Linking task. Datasets and codes are released at https://github.com/HITsz-TMG/Hansel.