OpenRaiser/Intern-Atlas huggingface dataset:
Web of scholars: A scholar knowledge graph
Authors: Jiaying Liu 0006, Jing Ren 0001, Wenqing Zheng, Lianhua Chi, Ivan Lee 0001, Feng Xia 0001
Venue: Conference SIGIR 2020
Year: 2020

In this work, we demonstrate a novel system, namely Web of Scholars, which integrates state-of-the-art mining techniques to search, mine, and visualize complex networks behind scholars in the field of Computer Science. Relying on the knowledge graph, it provides services for fast, accurate, and intelligent semantic querying as well as powerful recommendations. In addition, in order to realize information sharing, it provides open API to be served as the underlying architecture for advanced functions. Web of Scholars takes advantage of knowledge graph, which means that it will be able to access more knowledge if more search exist. It can be served as a useful and interoperable tool for scholars to conduct in-depth analysis within Science of Science.
Balancing constraints and rewards with meta-gradient d4pg
Authors: Dan A. Calian, Daniel J. Mankowitz, Tom Zahavy, Zhongwen Xu, Junhyuk Oh, Nir Levine, Timothy A. Mann
Venue: Conference ICLR 2021
Year: 2021

Deploying Reinforcement Learning (RL) agents to solve real-world applications often requires satisfying complex system constraints. Often the constraint thresholds are incorrectly set due to the complex nature of a system or the inability to verify the thresholds offline (e.g, no simulator or reasonable offline evaluation procedure exists). This results in solutions where a task cannot be solved without violating the constraints. However, in many real-world cases, constraint violations are undesirable yet they are not catastrophic, motivating the need for soft-constrained RL approaches. We present a soft-constrained RL approach that utilizes meta-gradients to find a good trade-off between expected return and minimizing constraint violations. We demonstrate the effectiveness of this approach by showing that it consistently outperforms the baselines across four different MuJoCo domains.
Ai for social impact: Results from deployments for public health and conversation
Authors: Milind Tambe
Venue: Conference KDD 2022
Year: 2022

With the maturing of AI and multiagent systems research, we have a tremendous opportunity to direct these advances towards addressing complex societal problems. I will focus on domains of public health and conservation, and address one key cross-cutting challenge: how to effectively deploy our limited intervention resources in these problem domains. I will present results from work around the globe in using AI for challenges in public health such as Maternal and Child care interventions, HIV prevention, and in conservation such as endangered wildlife protection. Achieving social impact in these domains often requires methodological advances. To that end, I will highlight key research advances in multiagent reasoning and learning, in particular in, restless multiarmed bandits, influence maximization in social networks, computational game theory and decision-focused learning. In pushing this research agenda, our ultimate goal is to facilitate local communities and non-profits to directly benefit from advances in AI tools and techniques
Adaptive popularity debiasing aggregator for graph collaborative filtering
Authors: Huachi Zhou, Hao Chen, Junnan Dong, Daochen Zha, Chuang Zhou, Xiao Huang
Venue: Conference SIGIR 2023
Year: 2023

The graph neural network-based collaborative filtering (CF) models user-item interactions as a bipartite graph and performs iterative aggregation to enhance performance. Unfortunately, the aggregation process may amplify the popularity bias, which impedes user engagement with niche (unpopular) items. While some efforts have studied the popularity bias in CF, they often focus on modifying loss functions, which can not fully address the popularity bias in GNN-based CF models. This is because the debiasing loss can be falsely backpropagated to non-target nodes during the backward pass of the aggregation. In this work, we study whether we can fundamentally neutralize the popularity bias in the aggregation process of GNN-based CF models. This is challenging because 1) estimating the effect of popularity is difficult due to the varied popularity caused by the aggregation from high-order neighbors, and 2) it is hard to train learnable popularity debiasing aggregation functions because of data sparsity. To this end, we theoretically analyze the cause of popularity bias and propose a quantitative metric, named inverse popularity score, to measure the effect of popularity in the representation space. Based on it, a novel graph aggregator named APDA is proposed to learn per-edge weight to neutralize popularity bias in aggregation. We further strengthen the debiasing effect with a weight scaling mechanism and residual connections. We apply APDA to two backbones and conduct extensive experiments on three real-world datasets. The results show that APDA significantly outperforms the state-of-the-art baselines in terms of recommendation performance and popularity debiasing.
Counting Graphlets: Space vs Time
Authors: Marco Bressan, Flavio Chierichetti, Ravi Kumar, Stefano Leucci, Alessandro Panconesi
Venue: Conference WSDM 2017
Year: 2017

Counting graphlets is a well-studied problem in graph mining and social network analysis. Recently, several papers explored very simple and natural approaches based on Monte Carlo sampling of Markov Chains (MC), and reported encouraging results. We show, perhaps surprisingly, that this approach is outperformed by a carefully engineered version of color coding (CC) [1], a sophisticated algorithmic technique that we extend to the case of graphlet sampling and for which we prove strong statistical guarantees. Our computational experiments on graphs with millions of nodes show CC to be more accurate than MC. Furthermore, we formally show that the mixing time of the MC approach is too high in general, even when the input graph has high conductance. All this comes at a price however. While MC is very efficient in terms of space, CC's memory requirements become demanding when the size of the input graph and that of the graphlets grow. And yet, our experiments show that a careful implementation of CC can push the limits of the state of the art, both in terms of the size of the input graph and of that of the graphlets.
Estimating graphlet statistics via lifting
Authors: Kirill Paramonov, Dmitry Shemetov, James Sharpnack
Venue: Conference KDD 2019
Year: 2019

Exploratory analysis over network data is often limited by the ability to efficiently calculate graph statistics, which can provide a model-free understanding of the macroscopic properties of a network. We introduce a framework for estimating the graphlet count---the number of occurrences of a small subgraph motif (e.g. a wedge or a triangle) in the network. For massive graphs, where accessing the whole graph is not possible, the only viable algorithms are those that make a limited number of vertex neighborhood queries. We introduce a Monte Carlo sampling technique for graphlet counts, called Lifting, which can simultaneously sample all graphlets of size up to k vertices for arbitrary k. This is the first graphlet sampling method that can provably sample every graphlet with positive probability and can sample graphlets of arbitrary size k. We outline variants of lifted graphlet counts, including the ordered, unordered, and shotgun estimators, random walk starts, and parallel vertex starts. We prove that our graphlet count updates are unbiased for the true graphlet count and have a controlled variance for all graphlets. We compare the experimental performance of lifted graphlet counts to the state-of-the art graphlet sampling procedures: Waddling and the pairwise subgraph random walk.
Question rewriting for open-domain conversational qa: Best practices and limitations
Authors: Marco Del Tredici, Gianni Barlacchi, Xiaoyu Shen, Weiwei Cheng, Adrià de Gispert
Venue: Conference CIKM 2021
Year: 2021

Open-domain conversational QA (ODCQA) calls for effective question rewriting (QR), as the questions in a conversation typically lack proper context for the QA model to interpret. In this paper, we compare two types of QR approaches, generative and expansive QR, in end-to-end ODCQA systems with recently released QReCC and OR-QuAC benchmarks. While it is common practice to apply the same QR approach for both the retriever and the reader in the QA system, our results show such strategy is generally suboptimal and suggest expansive QR is better for the sparse retriever and generative QR is better for the reader. Furthermore, while conversation history modeling with dense representations outperforms QR, we show the advantages to apply both jointly, as QR boosts the performance especially when limited history turns are considered.
Lecardv2: A large-scale chinese legal case retrieval dataset
Authors: Haitao Li, Yunqiu Shao, Yueyue Wu, Qingyao Ai, Yixiao Ma, Yiqun Liu
Venue: Conference SIGIR 2024
Year: 2024

As an important component of intelligent legal systems, legal case retrieval plays a critical role in ensuring judicial justice and fairness. However, the development of legal case retrieval technologies in the Chinese legal system is restricted by three problems in existing datasets: limited data size, narrow definitions of legal relevance, and naive candidate pooling strategies used in data sampling. To alleviate these issues, we introduce LeCaRDv2, a large-scale Legal Case Retrieval Dataset (version 2). It consists of 800 queries and 55,192 candidates extracted from 4.3 million criminal case documents. To the best of our knowledge, LeCaRDv2 is one of the largest Chinese legal case retrieval datasets, providing extensive coverage of criminal charges. Additionally, we enrich the existing relevance criteria by considering three key aspects: characterization, penalty, procedure. This comprehensive criteria enriches the dataset and may provides a more holistic perspective. Furthermore, we propose a two-level candidate set pooling strategy that effectively identify potential candidates for each query case. It's important to note that all cases in the dataset have been annotated by multiple legal experts specializing in criminal law. Their expertise ensures the accuracy and reliability of the annotations. We evaluate several state-of-the-art retrieval models at LeCaRDv2, demonstrating that there is still significant room for improvement in legal case retrieval. The details of LeCaRDv2 can be found at the anonymous website https://github.com/THUIR/LeCaRDv2.
Hypergraph convolution based attributed hypergraph clustering
Authors: Barakeel Fanseu Kamhoua, Lin Zhang, Kaili Ma, James Cheng, Bo Li, Bo Han
Venue: Conference CIKM 2021
Year: 2021

Attributed Graph Clustering (AGC) and Attributed Hypergraph Clustering (AHC) are important topics in graph mining with many applications. For AGC, amongst the unsupervised methods that combine the graph structure with node attributes, graph convolution has been shown to achieve impressive results. However, the effects of graph convolution on AGC have not yet been adequately studied. In this paper, we show that graph convolution attempts to find the best trade-off between node attribute distance and the number of inter-cluster edges. On the one hand, we show that compared to clustering node attributes directly, graph convolution produces a greater distance between node attributes in the same cluster and a smaller distance between node attributes in different clusters (which is detrimental for clustering). On the other hand, we show that graph convolution benefits clustering by considerably reducing the number of edges among different clusters. We then extend our result on AGC to AHC and leverage the hypergraph convolution to propose an unsupervised, fast, and memory-efficient algorithm (GRAC) for AHC, which achieves excellent performance on popular supervised clustering measures.
Heterogeneous hypergraph embedding for graph classification
Authors: Xiangguo Sun, Hongzhi Yin, Bo Liu, Hongxu Chen, Jiuxin Cao, Yingxia Shao, Nguyen Quoc Viet Hung
Venue: Conference WSDM 2021
Year: 2021

Recently, graph neural networks have been widely used for network embedding because of their prominent performance in pairwise relationship learning. In the real world, a more natural and common situation is the coexistence of pairwise relationships and complex non-pairwise relationships, which is, however, rarely studied. In light of this, we propose a graph neural network-based representation learning framework for heterogeneous hypergraphs, an extension of conventional graphs, which can well characterize multiple non-pairwise relations. Our framework first projects the heterogeneous hypergraph into a series of snapshots and then we take the Wavelet basis to perform localized hypergraph convolution. Since the Wavelet basis is usually much sparser than the Fourier basis, we develop an efficient polynomial approximation to the basis to replace the time-consuming Laplacian decomposition. Extensive evaluations have been conducted and the experimental results show the superiority of our method. In addition to the standard tasks of network embedding evaluation such as node classification, we also apply our method to the task of spammers detection and the superior performance of our framework shows that relationships beyond pairwise are also advantageous in the spammer detection. To make our experiment repeatable, source codes and related datasets are available at https://xiangguosun.mystrikingly.com
Contrastive learning of temporal distinctiveness for survival analysis in electronic health records
Authors: Mohsen Nayebi Kerdabadi, Arya Hadizadeh Moghaddam, Bin Liu, Mei Liu, Zijun Yao
Venue: Conference CIKM 2023
Year: 2023

Survival analysis plays a crucial role in many healthcare decisions, where the risk prediction for the events of interest can support an informative outlook for a patient's medical journey. Given the existence of data censoring, an effective way of survival analysis is to enforce the pairwise temporal concordance between censored and observed data, aiming to utilize the time interval before censoring as partially observed time-to-event labels for supervised learning. Although existing studies mostly employed ranking methods to pursue an ordering objective, contrastive methods which learn a discriminative embedding by having data contrast against each other, have not been explored thoroughly for survival analysis. Therefore, in this paper, we propose a novel Ontology-aware Temporality-based Contrastive Survival (OTCSurv) analysis framework that utilizes survival durations from both censored and observed data to define temporal distinctiveness and construct negative sample pairs with adjustable hardness for contrastive learning. Specifically, we first use an ontological encoder and a sequential self-attention encoder to represent the longitudinal EHR data with rich contexts. Second, we design a temporal contrastive loss to capture varying survival durations in a supervised setting through a hardness-aware negative sampling mechanism. Last, we incorporate the contrastive task into the time-to-event predictive task with multiple loss components. We conduct extensive experiments using a large EHR dataset to forecast the risk of hospitalized patients who are in danger of developing acute kidney injury (AKI), a critical and urgent medical condition. The effectiveness and explainability of the proposed model are validated through comprehensive quantitative and qualitative studies.
Ovqa: A clinically generated visual question answering dataset
Authors: Yefan Huang, Xiaoli Wang, Feiyan Liu, Guofeng Huang
Venue: Conference SIGIR 2022
Year: 2022

Medical visual question answering (Med-VQA) is a challenging problem that aims to take a medical image and a clinical question about the image as input and output a correct answer in natural language. Current medical systems often require large-scale and high-quality labeled data for training and evaluation. To address the challenge, we present a new dataset, denoted by OVQA, which is generated from electronic medical records. We develop a semi-automatic data generation tool for constructing the dataset. First, medical entities are automatically extracted from medical records and filled into predefined templates for generating question and answer pairs. These pairs are then combined with medical images extracted from corresponding medical records, to generate candidates for visual question answering (VQA). The candidates are finally verified with high-quality labels annotated by experienced physicians. To evaluate the quality of OVQA, we conduct comprehensive experiments on state-of-the-art methods for the Med-VQA task to our dataset. The results show that our OVQA can be used as a benchmarking dataset for evaluating existing Med-VQA systems. The dataset can be downloaded from http://47.94.174.82/.
Human preferences as dueling bandits
Authors: Xinyi Yan, Chengxi Luo, Charles L. A. Clarke, Nick Craswell, Ellen M. Voorhees, Pablo Castells
Venue: Conference SIGIR 2022
Year: 2022

The dramatic improvements in core information retrieval tasks engendered by neural rankers create a need for novel evaluation methods. If every ranker returns highly relevant items in the top ranks, it becomes difficult to recognize meaningful differences between them and to build reusable test collections. Several recent papers explore pairwise preference judgments as an alternative to traditional graded relevance assessments. Rather than viewing items one at a time, assessors view items side-by-side and indicate the one that provides the better response to a query, allowing fine-grained distinctions. If we employ preference judgments to identify the probably best items for each query, we can measure rankers by their ability to place these items as high as possible. We frame the problem of finding best items as a dueling bandits problem. While many papers explore dueling bandits for online ranker evaluation via interleaving, they have not been considered as a framework for offline evaluation via human preference judgments. We review the literature for possible solutions. For human preference judgments, any usable algorithm must tolerate ties, since two items may appear nearly equal to assessors, and it must minimize the number of judgments required for any specific pair, since each such comparison requires an independent assessor. Since the theoretical guarantees provided by most algorithms depend on assumptions that are not satisfied by human preference judgments, we simulate selected algorithms on representative test cases to provide insight into their practical utility. Based on these simulations, one algorithm stands out for its potential. Our simulations suggest modifications to further improve its performance. Using the modified algorithm, we collect over 10,000 preference judgments for pools derived from submissions to the TREC 2021 Deep Learning Track, confirming its suitability. We test the idea of best-item evaluation and suggest ideas for further theoretical and practical progress.
Beyond fine-tuning: Efficient and effective fed-tuning for mobile/web users
Authors: Bingyan Liu, Yifeng Cai, Hongzhe Bi, Ziqi Zhang, Ding Li, Yao Guo, Xiangqun Chen
Venue: Conference WWW 2023
Year: 2023

Fine-tuning is a typical mechanism to achieve model adaptation for mobile/web users, where a model trained by the cloud is further retrained to fit the target user task. While traditional fine-tuning has been proved effective, it only utilizes local data to achieve adaptation, failing to take advantage of the valuable knowledge from other mobile/web users. In this paper, we attempt to extend the local-user fine-tuning to multi-user fed-tuning with the help of Federated Learning (FL). Following the new paradigm, we propose EEFT, a framework aiming to achieve Efficient and Effective Fed-Tuning for mobile/web users. The key idea is to introduce lightweight but effective adaptation modules to the pre-trained model, such that we can freeze the pre-trained model and just focus on optimizing the modules to achieve cost reduction and selective task cooperation. Extensive experiments on our constructed benchmark demonstrate the effectiveness and efficiency of the proposed framework.
Federated node classification over graphs with latent link-type heterogeneity
Authors: Han Xie, Li Xiong, Carl Yang
Venue: Conference WWW 2023
Year: 2023

Federated learning (FL) aims to train powerful and generalized global models without putting distributed data together, which has been shown effective in various domains of machine learning. The non-IIDness of data across local clients has been a major challenge for FL. In graphs, one specifically important perspective of non-IIDness is manifested in the link-type heterogeneity underlying homogeneous graphs– the seemingly uniform links captured in most real-world networks can carry different levels of homophily or semantics of relations, while the exact sets and distributions of such latent link-types can further differ across local clients. Through our preliminary data analysis, we are motivated to design a new graph FL framework that can simultaneously discover latent link-types and model message-passing w.r.t. the discovered link-types through the collaboration of distributed local clients. Specifically, we propose a framework FedLit that can dynamically detect the latent link-types during FL via an EM-based clustering algorithm and differentiate the message-passing through different types of links via multiple convolution channels. For experiments, we synthesize multiple realistic datasets of graphs with latent heterogeneous link-types from real-world data, and partition them with different levels of link-type heterogeneity. Comprehensive experimental results and in-depth analysis have demonstrated both superior performance and rational behaviors of our proposed techniques.
Federated sparql query processing over heterogeneous linked data fragments
Authors: Lars Heling, Maribel Acosta
Venue: Conference WWW 2022
Year: 2022

Linked Data Fragments (LDFs) are Web interfaces that enable querying knowledge graphs on the Web. These interfaces, such as SPARQL endpoints or Triple Pattern Fragment servers, differ in the SPARQL expressions they can evaluate and the metadata they provide. So far, federated query processing has focused on federations with a single type of LDF interface, typically SPARQL endpoints. In this work, we address the challenges of SPARQL query processing over federations with heterogeneous LDF interfaces. To this end, we propose an interface-aware framework and illustrate its applicability with a prototypical approach. The results over the FedBench benchmark show a substantial improvement in performance by devising this interface-aware approach that exploits the capabilities of heterogeneous interfaces in federations.