OpenRaiser/Intern-Atlas huggingface dataset:
DisenKGAT: Knowledge Graph Embedding with Disentangled Graph Attention Network
Authors: Junkang Wu, Wentao Shi, Xuezhi Cao, Jiawei Chen, Wenqiang Lei, Fuzheng Zhang, Wei Wu, Xiangnan He
Venue: CIKM
Year: 2021

Knowledge graph completion (KGC) has become a focus of attention across deep learning community owing to its excellent contribution to numerous downstream tasks. Although recently have witnessed a surge of work on KGC, they are still insufficient to accurately capture complex relations, since they adopt the single and static representations. In this work, we propose a novel Disentangled Knowledge Graph Attention Network (DisenKGAT) for KGC, which leverages both micro-disentanglement and macro-disentanglement to exploit representations behind Knowledge graphs (KGs). To achieve micro-disentanglement, we put forward a novel relation-aware aggregation to learn diverse component representation. For macro-disentanglement, we leverage mutual information as a regularization to enhance independence. With the assistance of disentanglement, our model is able to generate adaptive representations in terms of the given scenario. Besides, our work has strong robustness and flexibility to adapt to various score functions. Extensive experiments on public benchmark datasets have been conducted to validate the superiority of DisenKGAT over existing methods in terms of both accuracy and explainability.
DynSTGAT: Dynamic Spatial-Temporal Graph Attention Network for Traffic Signal Control
Authors: Libing Wu, Min Wang, Dan Wu, Jia Wu
Venue: CIKM
Year: 2021

Adaptive traffic signal control plays a significant role in the construction of smart cities. This task is challenging because of many essential factors, such as cooperation among neighboring intersections and dynamic traffic scenarios. First, to facilitate the cooperation of traffic signals, existing work adopts graph neural networks to incorporate the temporal and spatial influences of the surrounding intersections into the target intersection, where spatial-temporal information is used separately. However, one drawback of these methods is that the spatial-temporal correlations are not adequately exploited to obtain a better control scheme. Second, in a dynamic traffic environment, the historical state of the intersection is also critical for predicting future signal switching. Previous work mainly solves this problem using the current intersection's state, neglecting the fact that traffic flow is continuously changing both spatially and temporally and does not handle the historical state. In this paper, we propose a novel neural network framework named DynSTGAT, which integrates dynamic historical state into a new spatial-temporal graph attention network to address the above two problems. More specifically, our DynSTGAT model employs a novel multi-head graph attention mechanism, which aims to adequately exploit the joint relations of spatial-temporal information. Then, to efficiently utilize the historical state information of the intersection, we design a sequence model with the temporal convolutional network (TCN) to capture the historical information and further merge it with the spatial information to improve its performance. Extensive experiments conducted in the multi-intersection scenario on synthetic data and real-world data confirm that our method can achieve superior performance in travel time and throughput against the state-of-the-art methods.
iMap: Incremental Node Mapping between Large Graphs Using GNN
Authors: Yikuan Xia, Jun Gao, Bin Cui
Venue: CIKM
Year: 2021

Node mapping between large graphs (or network alignment) plays a key preprocessing role in joint-graph data mining applications like social link prediction, cross-platform recommendation, etc. Most existing approaches attempt to perform alignment at the granularity of entire graphs, while handling the whole graphs may lower the scalability and the noisy nodes/edges in the graphs may impact the effectiveness. From the observation that potential node mappings always appear near known corresponding nodes, we propose iMAP, a novel sub-graph expansion based alignment framework to incrementally construct meaningful sub-graphs and perform alignment on each sub-graph pair iteratively, which reduces the unnecessary computation cost in the original raw networks and improves effectiveness via excluding possible noises. Specifically, iMap builds a candidate sub-graph around known matched nodes initially. In each following iteration, iMap trains an alignment model to infer the node mapping relationship between sub-graphs, from which the sub-graphs are further extended and refined. In addition, we design a Graph Neural Network(GNN) based model named MAP on each sub-graph pair in the iMap framework. MAP utilizes trainable Multi-layer Perception (MLP) prediction heads for similarity computation and employs a mixed loss function consisting of the ranking loss for contrastive learning and the cross-entropy loss for classification. Extensive experiments conducted on real social networks demonstrate superior efficiency and effectiveness (above 12% improvement) of our proposed method compared to several state-of-the-art methods.
Incremental Graph Convolutional Network for Collaborative Filtering
Authors: Jiafeng Xia, Dongsheng Li, Hansu Gu, Tun Lu, Peng Zhang, Ning Gu
Venue: CIKM
Year: 2021

Graph neural networks (GNN) recently achieved huge success in collaborative filtering (CF) due to the useful graph structure information. However, users will continuously interact with items, which causes the user-item interaction graphs to change over time and well-trained GNN models to be out-of-date soon. Naive solutions such as periodic retraining lose important temporal information and are computationally expensive. Recent works that leverage recurrent neural networks to keep GNN up-to-date may suffer from the "catastrophic forgetting'' issue, and experience a cold start with new users and items. To this end, we propose the incremental graph convolutional network (IGCN) --- a pure graph convolutional network (GCN) based method to update GNN models when new user-item interactions are available. IGCN consists of two main components: 1) a historical feature generation layer, which generates the initial user/item embedding via model agnostic meta-learning and ensures good initial states and fast model adaptation; 2) a temporal feature learning layer, which first aggregates the features from local neighborhood to update the embedding of each user/item within each subgraph via graph convolutional network and then fuses the user/item embeddings from last subgraph and current subgraph via incremental temporal convolutional network. Experimental studies on real-world datasets show that IGCN can outperform state-of-the-art CF algorithms in sequential recommendation tasks.
Crawler Detection in Location-Based Services Using Attributed Action Net
Authors: Wei Xia, Fei Zhao, Haishuai Wang, Peng Zhang, Anhui Wang, Kang Li
Venue: CIKM
Year: 2021

Malicious Web crawlers threaten information system due to heavily taking up bandwidth resources and stealing private user data. Ele.me, a prevalent on-demand food delivery platform in China, suffers from the negative impact of crawlers. The crawler detection systems face two major challenges: spatial patterns of the crawler behaviors and limited labeled data for training. In this paper, we present efficient solutions to tackle these challenges. Specifically, we propose a new Attributed Action Net (AANet for short) model to detect Location-Based Services~(LBS) crawlers and a three-stage learning framework to train the model. AANet consists of three different embedding modules, including the action token sequence, temporal-spatial attributes of users, and the context information of the raw data. We have deployed the model at Ele.me, and both offline experiments and online A/B tests show that the proposed method is superior to the state-of-the-art models for sequence data classification on the food delivery platform.
Explore, Filter and Distill: Distilled Reinforcement Learning in Recommendation
Authors: Ruobing Xie, Shaoliang Zhang, Rui Wang, Feng Xia, Leyu Lin
Venue: CIKM
Year: 2021

Reinforcement learning (RL) has been verified in real-world list-wise recommendation. However, RL-based recommendation suffers from huge memory and computation costs due to its large-scale models. Knowledge distillation (KD) is an effective approach for model compression widely used in practice. However, RL-based models strongly rely on sufficient explorations on the enormous user-item space due to the data sparsity issue, which multiplies the challenges of KD with RL models. What the teacher should teach and how much the student should learn from each lesson need to be carefully designed. In this work, we propose a novel Distilled reinforcement learning framework for recommendation (DRL-Rec), which aims to improve both effectiveness and efficiency in list-wise recommendation. Specifically, we propose an Exploring and filtering module before the distillation, which decides what lessons the teacher should teach from both teachers' and students' aspects. We also conduct a Confidence-guided distillation at both output and intermediate levels with a list-wise KL divergence loss and a Hint loss, which aims to understand how much the student should learn for each lesson. We achieve significant improvements on both offline and online evaluations in a well-known recommendation system. DRL-Rec has been deployed on WeChat Top Stories for more than six months, affecting millions of users. The source codes are released in https://github.com/modriczhang/DRL-Rec.
PATROL: A Velocity Control Framework for Autonomous Vehicle via Spatial-Temporal Reinforcement Learning
Authors: Zhi Xu, Shuncheng Liu, Ziniu Wu, Xu Chen, Kai Zeng, Kai Zheng, Han Su
Venue: CIKM
Year: 2021

The largest portion of urban congestion is caused by 'phantom' traffic jams, causing significant delay travel time, fuel waste, and air pollution. It frequently occurs in high-density traffics without any obvious signs of accidents or roadworks. The root cause of 'phantom' traffic jams in one-lane traffics is the sudden change in velocity of some vehicles (i.e. harsh driving behavior (HDB)), which may generate a chain reaction with accumulated impact throughout the vehicles along the lane. This paper makes the first attempt to address this notorious problem in a one-lane traffic environment through velocity control of autonomous vehicles. Specifically, we propose a velocity control framework, called PATROL (sPAtial-temporal ReinfOrcement Learning). First, we design a spatial-temporal graph inside the reinforcement learning model to process and extract the information (e.g. velocity and distance difference) of multiple vehicles ahead across several historical time steps in the interactive environment. Then, we propose an attention mechanism to characterize the vehicle interactions and an LSTM structure to understand the vehicles' driving patterns through time. At last, we modify the reward function used in previous velocity control works to enable the autonomous driving agent to predict the HDB of preceding vehicles and smoothly adjust its velocity, which could alleviate the chain reaction caused by HDB. We conduct extensive experiments to demonstrate the effectiveness and superiority of PATROL in alleviating the 'phantom' traffic jam in simulation environments. Further, on the real-world velocity control dataset, our method significantly outperforms the existing methods in terms of driving safety, comfortability, and efficiency.
Jura: Towards Automatic Compliance Assessment for Annual Reports of Listed Companies
Authors: Zhengqi Xu, Yixuan Cao, Rongyu Cao, Guoxiang Li, Xuanqiang Liu, Yan Pang, Yangbin Wang, Jianfei Zhang, Allie Cheung, Matthew Tam, Lukas Petrikas, Ping Luo
Venue: CIKM
Year: 2021

The initial public offering (IPO) market in Hong Kong is consistently one of the largest in the world. As part of its regulatory responsibilities, Hong Kong Exchanges and Clearing Limited (HKEX) reviews annual reports published by listed companies (issuers). The number of issuers has grown at a fast pace, reaching 2,538 as the end of 2020. This poses a challenge for manually reviewing these annual reports against the many diverse regulatory obligations (listing rules). We propose a system named Jura to improve the efficiency of annual report reviewing with the help of machine learning methods. This system checks the compliance of an issuer's published information against listing rules in four steps: panoptic document recognition, relevant passage location, fine-grained information extraction, and compliance assessment. This paper introduces in detail the passage location step, how it is critical for speeding up compliance assessment, and the various challenges faced. We argue that although a passage is a relatively independent unit, it needs to be combined with document structure and contextual information to accurately locate the relevant passages. With the help of Jura, HKEX reports saving 80% of the time on reviewing issuers' annual reports.
Expanding Relationship for Cross Domain Recommendation
Authors: Kun Xu, Yuanzhen Xie, Liang Chen, Zibin Zheng
Venue: CIKM
Year: 2021

Cross-domain recommendation technique is a promising way to alleviate data sparsity issues by transferring knowledge from an auxiliary domain to a target domain. However, most existing works focus on utilizing the same users among different domains, while ignoring domain-specific users which forms the majority in real-world circumstances. In this paper, we propose a novel cross-domain learning approach--Relation Expansion based Cross-Domain Recommendation (ReCDR) to improve recommendation accuracies on small-overlapped domains. ReCDR first models the interactions in each domain as a local graph. It then forms a shared network by expanding out relationships using pre-trained node similarities. On the enhanced graph, ReCDR adopts a hierarchical attention mechanism. The output embedding will finally be combined with the local feature to balance the result for dual-target task. The proposed model is thoroughly evaluated on three real-world datasets. Experiments demonstrate superior performance compared to state-of-the-art methods.
Topic-aware Heterogeneous Graph Neural Network for Link Prediction
Authors: Siyong Xu, Cheng Yang, Chuan Shi, Yuan Fang, Yuxin Guo, Tianchi Yang, Luhao Zhang, Maodi Hu
Venue: CIKM
Year: 2021

Heterogeneous graphs (HGs), consisting of multiple types of nodes and links, can characterize a variety of real-world complex systems. Recently, heterogeneous graph neural networks (HGNNs), as a powerful graph embedding method to aggregate heterogeneous structure and attribute information, has earned a lot of attention. Despite the ability of HGNNs in capturing rich semantics which reveal different aspects of nodes, they still stay at a coarse-grained level which simply exploits structural characteristics. In fact, rich unstructured text content of nodes also carries latent but more fine-grained semantics arising from multi-facet topic-aware factors, which fundamentally manifest why nodes of different types would connect and form a specific heterogeneous structure. However, little effort has been devoted to factorizing them. In this paper, we propose a Topic-aware Heterogeneous Graph Neural Network, named THGNN, to hierarchically mine topic-aware semantics for learning multi-facet node representations for link prediction in HGs. Specifically, our model mainly applies an alternating two-step aggregation mechanism including intra-metapath decomposition and inter-metapath mergence, which can distinctively aggregate rich heterogeneous information according to the inferential topic-aware factors and preserve hierarchical semantics. Furthermore, a topic prior guidance module is also designed to keep the quality of multi-facet topic-aware embeddings relying on the global knowledge from unstructured text content in HGs. It helps to simultaneously improve both performance and interpretability. Experimental results on three real-world HGs demonstrate that our proposed model can effectively outperform the state-of-the-art methods in the link prediction task, and show the potential interpretability of learnt multi-facet topic-aware representations.
DESTINE: Dense Subgraph Detection on Multi-Layered Networks
Authors: Zhe Xu, Si Zhang, Yinglong Xia, Liang Xiong, Jiejun Xu, Hanghang Tong
Venue: CIKM
Year: 2021

Dense subgraph detection is a fundamental building block for a variety of applications. Most of the existing methods aim to discover dense subgraphs within either a single network or a multi-view network while ignoring the informative node dependencies across multiple layers of networks in a complex system. To date, it largely remains a daunting task to detect dense subgraphs on multi-layered networks. In this paper, we formulate the problem of dense subgraph detection on multi-layered networks based on cross-layer consistency principle. We further propose a novel algorithm DESTINE based on projected gradient descent with the following advantages. First, armed with the cross-layer dependencies, DESTINE is able to detect significantly more accurate and meaningful dense subgraphs at each layer. Second, it scales linearly w.r.t. the number of links in the multi-layered network. Extensive experiments demonstrate the efficacy of the proposed DESTINE algorithm in various cases.
A Chinese Knowledge Base Question Answering System
Authors: Xiaona Xue, Jinling Jiang, Wenjian Zhang, Yanxiang Huang, Xindong Wu
Venue: CIKM
Year: 2021

This paper presents a HAO-Interaction question answering system, which exploits knowledge based question answering (KBQA) technology to quickly obtain an answer path for the input question, and then a creative text generation mechanism to acquire the final answer text. The system also provides visibility of the answer path on the user interface in order to facilitate user understanding. Different from other KBQA systems, HAO-Interaction supports users to incorporate an organizational graph database while accessing all system functionalities. In addition, the answer generation solution implemented in the system does not require any training data. HAO-Interaction keeps low response latency while ensuring a high user satisfaction. The effectiveness of HAO-Interaction has been verified by analyzing thousands of user reviews collected by the system.
Contextual Skill Proficiency via Multi-task Learning at LinkedIn
Authors: Xiao Yan, Ada Ma, Jaewon Yang, Lin Zhu, How Jing, Jacob Bollinger, Qi He
Venue: CIKM
Year: 2021

The ability to infer an individual's expertise for a given skill has proven to be crucial in creating economic opportunity for every talent of the global workforce. Applications ranging from recommending relevant job opportunities to talents to providing better candidate suggestions to recruiters, all benefit from deep understanding of the skill "proficiency" of the talent pool. LinkedIn's "Skill" profile section can be leveraged in this expert finding task. Whereas it is easy to incentivize members to put skills on their profile, estimating members' expertise is much more challenging for several reasons. First, the collection of ground-truth data at scale can be expensive and challenging. Second, "being proficient at a certain skill" can have very different meaning in different contexts -- a professor in machine learning having deep theoretical knowledge might lack the practical skill for implementing a large-scale recommendation system unlike experienced ML practitioners. We present our proposed framework to infer a member's expertise in a certain skill based upon a multi-view, multi-task learning scheme that incorporates signals from multiple contexts. We show the efficacy of the proposed framework with offline evaluation results as well as online A/B testing in multiple products, from finding experts among friends, to recommending jobs to qualified members. We also show that our estimated proficiency can help alleviate the cold-start problem when applied to a new context (i.e., through transfer learning) where only a small amount of labeled data is needed to achieve reasonable performance. Finally, we share the insights that demonstrate the talent market is shocked disproportionately among members with different skill proficiency levels by COVID-19.
CADRE: A Cloud-Based Data Service for Big Bibliographic Data
Authors: Xiaoran Yan, Guangchen Ruan, Dimitar Nikolov, Matthew Hutchinson, Chathuri Peli Kankanamalage, Benjamin Serrette, James R. McCombs, Alan Walsh, Esen Tuna, Valentin Pentchev
Venue: CIKM
Year: 2021

Large bibliographic data sets hold the promise of revolutionizing the scientific enterprise when combined with state-of-the-science computational capabilities. Providing high-quality data services for large network datasets such as the Microsoft Academic Graph, which contains more than two billion citation links, poses significant difficulties for universities. Data systems based on the property graph model are capable of delivering efficient graph query services for large networks. However, real-life queries often combine multiple types of data models. To satisfy the needs of different user groups, we developed and deployed a cloud-based data system consisting of scalable graph and text-indexed query engines. For non-expert users, the property graph model also presents a technological barrier. To alleviate the steep learning curve, we designed an intuitive graphical user interface for query-building. For advanced users, a scalable notebook service in our platform provides a more flexible computing environments where the query results can be further analyzed. These systems form the data-backbone of the Collaborative Archive and Data Research Environment (CADRE), which provides efficient and high-quality bibliographic data services to eleven large public universities in North America.
Binary Code based Hash Embedding for Web-scale Applications
Authors: Bencheng Yan, Pengjie Wang, Jinquan Liu, Wei Lin, Kuang-Chih Lee, Jian Xu, Bo Zheng
Venue: CIKM
Year: 2021

Nowadays, deep learning models are widely adopted in web-scale applications such as recommender systems, and online advertising. In these applications, embedding learning of categorical features is crucial to the success of deep learning models. In these models, a standard method is that each categorical feature value is assigned a unique embedding vector which can be learned and optimized. Although this method can well capture the characteristics of the categorical features and promise good performance, it can incur a huge memory cost to store the embedding table, especially for those web-scale applications. Such a huge memory cost significantly holds back the effectiveness and usability of EDRMs. In this paper, we propose a binary code based hash embedding method which allows the size of the embedding table to be reduced in arbitrary scale without compromising too much performance. Experimental evaluation results show that one can still achieve 99% performance even if the embedding table size is reduced 1000× smaller than the original one with our proposed method.