OpenRaiser/Intern-Atlas huggingface dataset:
Unsupervised Robust Cross-Lingual Entity Alignment via Neighbor Triple Matching with Entity and Relation Texts
Authors: Soojin Yoon, Sungho Ko, Tongyoung Kim, SeongKu Kang, Jinyoung Yeo, Dongha Lee
Venue: WSDM
Year: 2025

Cross-lingual entity alignment (EA) enables the integration of multiple knowledge graphs (KGs) across different languages, providing users with seamless access to diverse and comprehensive knowledge. Existing methods, mostly supervised, face challenges in obtaining labeled entity pairs. To address this, recent studies have shifted towards self-supervised and unsupervised frameworks. Despite their effectiveness, these approaches have limitations: (1) Relation passing: mainly focusing on the entity while neglecting the semantic information of relations, (2) Isomorphic assumption: assuming isomorphism between source and target graphs, which leads to noise and reduced alignment accuracy, and (3) Noise vulnerability: susceptible to noise in the textual features, especially when encountering inconsistent translations or Out-of-Vocabulary (OOV) problems. In this paper, we propose ERAlign, an unsupervised and robust cross-lingual EA pipeline that jointly performs Entity-level and Relation-level Alignment by neighbor triple matching strategy using semantic textual features of relations and entities. Its refinement step iteratively enhances results by fusing entity-level and relation-level alignments based on neighbor triple matching. The additional verification step examines the entities' neighbor triples as the linearized text. This Align-then-Verify pipeline rigorously assesses alignment results, achieving near-perfect alignment even in the presence of noisy textual features of entities. Our extensive experiments demonstrate that the robustness and general applicability of ERAlign improved the accuracy and effectiveness of EA tasks, contributing significantly to knowledge-oriented applications.
Explainable CTR Prediction via LLM Reasoning
Authors: Xiaohan Yu, Li Zhang, Chong Chen
Venue: WSDM
Year: 2025

Recommendation Systems have become integral to modern user experiences, but lack transparency in their decision-making processes. Existing explainable recommendation methods are hindered by reliance on a post-hoc paradigm, wherein explanation generators are trained independently of the underlying recommender models. This paradigm necessitates substantial human effort in data construction and raises concerns about explanation reliability. In this paper, we present ExpCTR, a novel framework that integrates large language model based explanation generation directly into the CTR prediction process. Inspired by recent advances in reinforcement learning, we employ two carefully designed reward mechanisms, LC alignment, which ensures explanations reflect user intentions, and IC alignment, which maintains consistency with traditional ID-based CTR models. Our approach incorporates an efficient training paradigm with LoRA and a three-stage iterative process. ExpCTR circumvents the need for extensive explanation datasets while fostering synergy between CTR prediction and explanation generation. Experimental results demonstrate that ExpCTR significantly enhances both recommendation accuracy and interpretability across three real-world datasets.
Bearing Power Loss Predictions in Wind Turbine Gearbox: An Approach Based on LLMs
Authors: Janice Anta Zebaze, Azanzi Jiomekong, Innocent Souopgui, Germaine Djuidje Kenmoe
Venue: WSDM
Year: 2025

A constant and consistent supply in electrical energy in a location is a reflection of a good economy. Developing countries nevertheless don't have access to this quality of energy, which slows down their economy and consequently development. Wind is a clean, sustainable and renewable resource which can be used to meet the energy needs in such countries. However, the intermittent nature of wind yields fluctuations on the amount of energy produced by a wind turbine. Coupled with frictional power losses in the wind turbine gearbox bearings, one can't be sure on the exact amount of energy that will be produced. This leads to the distribution and management issues. To tackle this issue, we propose here the use of Large Language models. These are tools which have been proving their potential in various domains till date and whose potential are still to be seen in the field to our knowledge. Taking advantage of their flexibility and adaptability to any model and dataset, we intend to explore its abilities in the fields of wind energy and tribology. Making use of available data, predictions on the wind energy potential and power losses will be carried out using Large Language models such as BERT. The results of this work intends to promote the use of wind energy by lifting barriers in thee management and knowledge of the resource.
Robustness Verification of Deep Graph Neural Networks Tightened by Linear Approximation
Authors: Xingyu Zeng, Han Li, Qi Qi, Jingyu Wang, Haodong Deng, Haifeng Sun, Zirui Zhuang, Jianxin Liao
Venue: WSDM
Year: 2025

Recent research indicates that adding residual connections in Graph Neural Networks (GNNs) would amplify susceptibility to anomalous nodes, consequently undermining the robustness of deep GNNs in practical settings. However, existing verification methods encounter challenges with the increasing number of parameters and computational overhead in deep GNNs. In this paper, we derive the general form of the residual connections and apply the dual backpropagation network to deep GNNs. Considering the heightened computational errors arising from the increased number of layers in deep GNNs, we propose a new method for calculating intermediate activation bounds of GNNs based on linear approximation. Experimental results show that new method can effectively enhance the verification accuracy. Notably, the maximum perturbation value of nodes correctly classified shows an average improvement of 119.5%. To showcase the the efficacy and scalability of our method, we verify robustness of deep GNNs on six different graph datasets, and our method can effectively verify the robustness of deep GNNs even with 32 layers of residual connections, i.e. verify over 87.29% of nodes in the Citeseer dataset. Furthermore, we analyse the influence of the graph structural properties on the robustness of the model.
DeMBR: Denoising Model with Memory Pruning and Semantic Guidance for Multi-Behavior Recommendation
Authors: Shuai Zhang, Hua Chu, Jianan Li, Yangtao Zhou, Shirong Wang, Qiaofei Sun
Venue: WSDM
Year: 2025

Multi-behavior recommendation systems aim to incorporate auxiliary behaviors (e.g., click, cart, etc.) to enhance the understanding of sparse target behaviors (e.g., purchase), thereby capturing user preferences more accurately. Currently, multi-behavior recommendation research focuses on modeling the associations between different user behaviors, but ignores the large amount of noise in user interaction data. This noise may come from accidental touches, curiosity, or ineffective operations during the purchasing process, and can be further categorized into two types: 1) hard noise is significantly deviates from the user's true preferences, and 2) soft noise is closer to the user's true preferences. The presence of noise can interfere with the model's ability to accurately identify the user's true preferences. To overcome the aforementioned issue, we innovatively propose a Denoising Model with Memory Pruning and Semantic Guidance for Multi-Behavior Recommendation (DeMBR). The model eliminates different types of noise at the data level and the representation level, respectively. Specifically, since hard noise significantly deviates from user preferences, we design a pruning-based denoising module that leverages a memory bank, which identifies and removes hard noise interactions from the data. Since soft noise reflects some user preferences, we design a semantic guidance denoising module that leverages behaviors with strong expressive ability (e.g., purchase) to guide those with weaker ability (e.g., click), effectively suppressing noise while preserving true's preferences. Finally, we designed a cross-learning module that allows noise-identifying signals to be exchanged between the two modules, and ultimately learn representations that accurately reflect user's preferences. Extensive experiments conducted on two public datasets demonstrate that our model substantially surpasses the state-of-the-art recommendation models. Our code is publicly available at: https://github.com/DeMBR2024/DeMBR.git
Privacy-Preserving Orthogonal Aggregation for Guaranteeing Gender Fairness in Federated Recommendation
Authors: Siqing Zhang, Yuchen Ding, Wei Tang, Wei Sun, Yong Liao, Peng Yuan Zhou
Venue: WSDM
Year: 2025

Under stringent privacy constraints, whether federated recommendation systems can achieve group fairness remains an inadequately explored question. Taking gender fairness as a representative issue, we identify three phenomena in federated recommendation systems: performance difference, data imbalance, and preference disparity. We discover that the state-of-the-art methods only focus on the first phenomenon. Consequently, their imposition of inappropriate fairness constraints detrimentally affects the model training. Moreover, due to insufficient sensitive attribute protection of existing works, we can infer the gender of all users with 99.90% accuracy even with the addition of maximal noise. In this work, we propose Privacy-Preserving Orthogonal Aggregation (PPOA), which employs the secure aggregation scheme and quantization technique, to prevent the suppression of minority groups by the majority and preserve the distinct preferences for better group fairness. PPOA can assist different groups in obtaining their respective model aggregation results through a designed orthogonal mapping while keeping their attributes private. Experimental results on three real-world datasets demonstrate that PPOA enhances recommendation effectiveness for both females and males by up to 8.25% and 6.36%, respectively, with a maximum overall improvement of 7.30%, and achieves optimal fairness in most cases. Extensive ablation experiments and visualizations indicate that PPOA successfully maintains preferences for different gender groups.
HACD: Harnessing Attribute Semantics and Mesoscopic Structure for Community Detection
Authors: Anran Zhang, Xingfen Wang, Yuhan Zhao
Venue: WSDM
Year: 2025

Community detection plays a pivotal role in uncovering closely connected subgraphs, aiding various real-world applications such as recommendation systems and anomaly detection. With the surge of rich information available for entities in real-world networks, the community detection problem in attributed networks has attracted widespread attention. While previous research has effectively leveraged network topology and attribute information for attributed community detection, these methods overlook two critical issues: (i) the semantic similarity between node attributes within the community, and (ii) the inherent mesoscopic structure, which differs from the pairwise connections of the micro-structure. To address these limitations, we propose HACD, a novel attributed community detection model based on heterogeneous graph attention networks. HACD treats node attributes as another type of node, constructs attributed networks into heterogeneous graph structures and employs attribute-level attention mechanisms to capture semantic similarity. Furthermore, HACD introduces a community membership function to explore mesoscopic community structures, enhancing the robustness of detected communities. Extensive experiments demonstrate the effectiveness and efficiency of HACD, outperforming state-of-the-art methods in attributed community detection tasks. Our code is publicly available at https://github.com/Anniran1/HACD1-wsdm https://github.com/Anniran1/HACD1-wsdm.
FedGF: Enhancing Structural Knowledge via Graph Factorization for Federated Graph Learning
Authors: Pengyang Zhou, Chaochao Chen, Weiming Liu, Xinting Liao, Fengyuan Yu, Zhihui Fu, Xingyu Lou, Wu Wen, Xiaolin Zheng, Jun Wang
Venue: WSDM
Year: 2025

Federated graph learning involves training graph neural networks distributively on local graphs and aggregating model parameters in a central server. However, existing methods fail to effectively capture and leverage the inherent global structures, hindering local structural modeling. To address this, we propose Federated Graph Factorization (FedGF), which enhances structural knowledge via privacy-preserving graph factorization. Specifically, FedGF includes three modules, i.e., global structure reconstruction (GSR), local structure exploration (LSE), and global-local structure alignment (GLSA). Firstly, GSR factorizes client graphs into a series of learnable graph atoms and conducts reconstruction to capture the globally shared structure. Then, LSE explores the local structure, mining potential but unrevealed connections within client subgraphs. GLSA further aligns the global and local structure to alternatively refine the graph atoms and GNN model, enhancing the overall structural modeling. Extensive experiments on six datasets consistently validate the effectiveness of \modelname.
Gradient Deconfliction via Orthogonal Projections onto Subspaces For Multi-task Learning
Authors: Shijie Zhu, Hui Zhao, Tianshu Wu, Pengjie Wang, Hongbo Deng, Jian Xu, Bo Zheng
Venue: WSDM
Year: 2025

Although multi-task learning (MTL) has been a preferred approach and successfully applied in many real-world scenarios, MTL models are not guaranteed to outperform single-task models on all tasks mainly due to the negative effects of conflicting gradients among the tasks. In this paper, we fully examine the influence of conflicting gradients and further emphasize the importance and advantages of achieving non-conflicting gradients which allows simple but effective trade-off strategies among the tasks with stable performance. Based on our findings, we propose the Gradient Deconfliction via Orthogonal Projections onto Subspaces (GradOPS) spanned by other task-specific gradients. Our method not only solves all conflicts among the tasks, but can also effectively search for diverse solutions towards different trade-off preferences among the tasks. Theoretical analysis on convergence is provided, and performance of our algorithm is fully testified on multiple benchmarks in various domains. Results demonstrate that our method can effectively find multiple state-of-the-art solutions with different trade-off strategies among the tasks on multiple datasets.
HHGT: Hierarchical Heterogeneous Graph Transformer for Heterogeneous Graph Representation Learning
Authors: Qiuyu Zhu, Liang Zhang, Qianxiong Xu, Kaijun Liu, Cheng Long, Xiaoyang Wang
Venue: WSDM
Year: 2025

Despite the success of Heterogeneous Graph Neural Networks (HGNNs) in modeling real-world Heterogeneous Information Networks (HINs), challenges such as expressiveness limitations and over-smoothing have prompted researchers to explore Graph Transformers (GTs) for enhanced HIN representation learning. However, research on GT in HINs remains limited, with two key shortcomings in existing work: (1) A node's neighbors at different distances in HINs convey diverse semantics; for instance, a paper's direct neighbor (a paper) in an academic graph signifies a citation relation, whereas the indirect neighbor (another paper) implies a thematic association, reflecting distinct meanings. Unfortunately, existing methods ignore such differences and uniformly treat neighbors within a given distance in a coarse manner, which results in semantic confusion. (2) Nodes in HINs have various types, each with unique semantics, e.g., papers and authors in an academic graph carry distinct meanings. Nevertheless, existing methods mix nodes of different types during neighbor aggregation, hindering the capture of proper correlations between nodes of diverse types. To bridge these gaps, we design an innovative structure named (k,t)-ring neighborhood, where nodes are initially organized by their distance, forming different non-overlapping k-ring neighborhoods for each distance. Within each k-ring structure, nodes are further categorized into different groups according to their types, thus emphasizing the heterogeneity of both distances and types in HINs naturally. Based on this structure, we propose a novel Hierarchical Heterogeneous Graph Transformer (HHGT) model, which seamlessly integrates a Type-level Transformer for aggregating nodes of different types within each k-ring neighborhood, followed by a Ring-level Transformer for aggregating different k-ring neighborhoods in a hierarchical manner. Extensive experiments are conducted on downstream tasks to verify HHGT's superiority over 14 baselines, with a notable improvement of up to 24.75% in NMI and 29.25% in ARI for node clustering task on the ACM dataset compared to the best baseline.
World Wide Web development at Notre Dame: it's easier to ask forgiveness than permission
Authors: M. W. Miller
Venue: WWW
Year: 1994

Article World Wide Web development at Notre Dame: it's easier to ask forgiveness than permission Share on Author: Mike W. Miller G038 Computing Center, Notre Dame, IN G038 Computing Center, Notre Dame, INView Profile Authors Info & Claims SIGUCCS '94: Proceedings of the 22nd annual ACM SIGUCCS conference on User servicesOctober 1994 Pages 169–172https://doi.org/10.1145/196355.196439Published:16 October 1994 0citation76DownloadsMetricsTotal Citations0Total Downloads76Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
IVth International Conference of the International Court of the Environment (ICE) — ‘Towards the World Governing of the Environment’ — Held at the Cini Foundation, S. Giorgo Island, Venice, Italy, during 2–5 June 1994
Authors: R. Charlier
Venue: WWW
Year: 1994

IVth International Conference of the International Court of the Environment (ICE) — ‘Towards the World Governing of the Environment’ — Held at the Cini Foundation, S. Giorgo Island, Venice, Italy, during 2–5 June 1994 - Volume 21 Issue 3
Making mothers: Missionaries, medical officers and women's work in colonial Asante, 1924-1945.
Authors: J. Allman
Venue: WWW
Year: 1994

Journal Article Making Mothers: Missionaries, Medical Officers and Women's Work in Colonial Asante, 1924–1945 Get access Jean Allman Jean Allman Search for other works by this author on: Oxford Academic Google Scholar History Workshop Journal, Volume 38, Issue 1, AUTUMN 1994, Pages 23–47, https://doi.org/10.1093/hwj/38.1.23 Published: 01 October 1994
Pathways to World Prehistory Presidential Address 1994
Authors: D. Harris
Venue: WWW
Year: 1994

Several previous Presidents, most notably Professor Sir Grahame Clark, have stressed in their presidential addresses the worldwide scope of our subject; but so far only one, my distinguished predecessor, Thurstan Shaw, has chosen to speak mainly about the prehistory of a non-European part of the world, in his case Africa (Shaw 1990). My aim today is to develop this theme by exploring three pathways to world prehistory: first, the pathway followed by the Society itself; second, the pathway that led humanity to people the world's continents; and third, the pathway that links prehistoric archaeology to the concerns of the modern world. This agenda may appear unrealistically ambitious for a short address, but, by briefly considering these three inter-twined pathways, I hope to show that the study of prehistory is not only worthwhile for its own sake, but that it also has direct relevance to the social and political problems of our late 20th century world. On the occasion of our 50th Anniversary Conference, held in Norwich in March 1985, Grahame Clark described the eventful history of the Society, and that of its predecessor, the Prehistoric Society of East Anglia. He analyzed the trend in membership and publication that signalled the Society's transformation, from a local band of mainly amateur enthusiasts to a national society of professional and amateur prehistorians, with, increasingly, an international view of their subject (Clark 1985). The sub-title of Professor Clark's paper — From East Anglia to the World — neatly encapsulated this theme, which he illustrated by analyzing the geographical coverage of articles published in the Proceedings between 1911 and 1982: an analysis that brought up-to-date the earlier examination of publication trends that he had included in his own Presidential Address in 1959 (Clark 1959).
Gender Inequalities and Demographic Behavior: India
Authors: S. Desai
Venue: WWW
Year: 1994

As India prepares for the 1994 International Conference on Population and Development (ICPD), it is clear that the country’s population policy faces a number of serious challenges. Although India was the first country to announce an official family planning program in 1952, its population has grown from 361 million in 1951 to 844 million in 1991. This is one of three reports on the relationship between gender equity, family structure and dynamics, and the achievement of reproductive choice prepared by the Population Council for the 1994 International Year of the Family and the 1994 ICPD. These reports provide critical reviews of the relationship between gender inequality and demographic behavior in three demographically significant, culturally distinct parts of the developing world: Egypt, India, and Ghana and Kenya. The purpose of the reports is to help governments and international agencies design and implement policies that are affirmative of women, sensitive to the family’s central role in resource allocation and distribution, and effective in achieving broad-based population and development goals.
World Bank Atlas, 25th Anniversary Edition . The World Bank, Washington, DC, USA: 36 pp., illustr., 28 × 23 × 0.4 cm, softbound, no price indicated, 1993.
Authors: R. Charlier
Venue: WWW
Year: 1994

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