OpenRaiser/Intern-Atlas huggingface dataset:
When Structure Meets Keywords: Cohesive Attributed Community Search
Authors: Yuanyuan Zhu, Jian He, Junhao Ye, Lu Qin, Xin Huang, Jeffrey Xu Yu
Venue: CIKM
Year: 2020

As an online, query-dependent variant of the well-known community detection problem, community search has been studied for years to find communities containing the query vertices. Along with the generation of graphs with rich attribute information, attributed community search has attracted increasing interest recently, aiming to select communities where vertices are cohesively connected and share homogeneous attributes. However, existing community models may include cut-edges/vertices and thus cannot well guarantee the strong connectivity required by a cohesive community. In this paper, we propose a new cohesive attributed community (CAC) model that can ensure both structure cohesiveness and attribute cohesiveness of communities. Specifically, for a query with vertex vq and keyword set S, we aim to find the cohesively connected communities containing vq with the most shared keywords in S. It is nontrivial as we need to explore all possible subsets of S to verify the existence of structure cohesive communities until we find the communities with the most common keywords. To tackle this problem, we make efforts in two aspects. The first is to reduce the candidate keyword subsets. We achieve this by exploring the anti-monotonicity and neighborhood-constraint properties of our CAC model so that we can filter out the unpromising keyword subsets. The second is to speed up the verification process for each candidate keyword subset. We propose two indexes TIndex and MTIndex to reduce the size of the candidate subgraph before the verification. Moreover, we derive two new properties based on these indexes to reduce the candidate keyword subsets further. We conducted extensive experimental studies on four real-world graphs and validated the effectiveness and efficiency of our approaches.
Towards Rich Qery Blockchain Database
Authors: Yanchao Zhu, Zhao Zhang, Cheqing Jin, Aoying Zhou, Gang Qin, Yingjie Yang
Venue: CIKM
Year: 2020

In this demo, we present SEBDB, a novel blockchain database that integrates immutability and transparency properties of blockchain with modeling and query ability of relational database. In summary, SEBDB has the following advantages: First, it adopts the linked structure and full replication of data among multiple participants to guarantee immutability and transparency. Second, it introduces the relational model to blockchain without introducing extra overhead, based on which relational queries are supported. SEBDB supports SQL-like language as the general interface to support convenient application development, in which intrinsic operations are re-defined and re-implemented to suit for blockchain platform. Third, it supports rich verifiable queries based on the proposed authenticated index, thin clients can participate in the system regardless of limitations of storage, network, and computing resources. We demonstrate the usability and scalability of SEBDB using a donation system.
Corpus Bootstrapping for Assessment of the Properties of Effectiveness Measures
Authors: Justin Zobel, Lida Rashidi
Venue: CIKM
Year: 2020

Bootstrapping is an established tool for examining the behaviour of offline information retrieval (IR) experiments, where it has primarily been used to assess statistical significance and the robustness of significance tests. In this work we consider how bootstrapping can be used to assess the reliability of effectiveness measures for experimental IR. We use bootstrapping of the corpus of documents rather than, as in most prior work, the set of queries. We demonstrate that bootstrapping can provide new insights into the behaviour of effectiveness measures: the precision of the measurement of a system for a query can be quantified; some measures are more consistent than others; rankings of systems on a test corpus likewise have a precision (or uncertainty) that can be quantified; and, in experiments with limited volumes of relevance judgements, measures can be wildly different in terms of reliability and precision. Our results show that the uncertainty in measurement and ranking of system performance can be substantial and thus our approach to corpus bootstrapping provides a key tool for helping experimenters to choose measures and understand reported outcomes.
Behavior-driven Student Performance Prediction with Tri-branch Convolutional Neural Network
Authors: Jian Zong, Chaoran Cui, Yuling Ma, Li Yao, Meng Chen, Yilong Yin
Venue: CIKM
Year: 2020

Student performance prediction aims to leverage student-related information to predict their future academic outcomes, which may be beneficial to numerous educational applications, such as personalized teaching and academic early warning. In this paper, we seek to address the problem by analyzing students' daily studying and living behavior, which is comprehensively recorded via campus smart cards. Different from previous studies, we propose an end-to-end student performance prediction model, namely Tri-branch CNN, which is equipped with three types of convolutional filters, i.e., the row-wise convolution, column-wise convolution, and group-wise convolution, to effectively capture the duration, periodicity, and location-aware characteristic of student behavior, respectively. We also introduce the attention mechanism and cost-sensitive learning strategy to further improve the accuracy of our approach. Extensive experiments on a large-scale real-world dataset demonstrate the potential of our approach for student performance prediction.
An Empirical Study on Clarifying Question-Based Systems
Authors: Jie Zou, Evangelos Kanoulas, Yiqun Liu
Venue: CIKM
Year: 2020

Search and recommender systems that take the initiative to ask clarifying questions to better understand users' information needs are receiving increasing attention from the research community. However, to the best of our knowledge, there is no empirical study to quantify whether and to what extent users are willing or able to answer these questions. In this work, we conduct an online experiment by deploying an experimental system, which interacts with users by asking clarifying questions against a product repository. We collect both implicit interaction behavior data and explicit feedback from users showing that: (a) users are willing to answer a good number of clarifying questions (11 on average), but not many more than that; (b) most users answer questions until they reach the target product, but also a fraction of them stops due to fatigue or due to receiving irrelevant questions; (c) part of the users' answers (17%) are actually opposite to the description of the target product; while (d) most of the users (84%) find the question-based system helpful towards completing their tasks. Some of the findings of the study contradict current assumptions on simulated evaluations in the field, while they point towards improvements in the evaluation framework and can inspire future interactive search/recommender system designs.
Exploiting Class Labels to Boost Performance on Embedding-based Text Classification
Authors: Arkaitz Zubiaga
Venue: CIKM
Year: 2020

Text classification is one of the most frequent tasks for processing textual data, facilitating among others research from large-scale datasets. Embeddings of different kinds have recently become the de facto standard as features used for text classification. These embeddings have the capacity to capture meanings of words inferred from occurrences in large external collections. While they are built out of external collections, they are unaware of the distributional characteristics of words in the classification dataset at hand, including most importantly the distribution of words across classes in training data. To make the most of these embeddings as features and to boost the performance of classifiers using them, we introduce a weighting scheme, Term Frequency-Category Ratio (TF-CR), which can weight high-frequency, category-exclusive words higher when computing word embeddings. Our experiments on eight datasets show the effectiveness of TF-CR, leading to improved performance scores over the well-known weighting schemes TF-IDF and KLD as well as over the absence of a weighting scheme in most cases.
VerSaChI: Finding Statistically Significant Subgraph Matches using Chebyshev's Inequality
Authors: Shubhangi Agarwal, Sourav Dutta, Arnab Bhattacharya
Venue: CIKM
Year: 2021

Approximate subgraph matching, an important primitive for many applications like question answering, community detection, and motif discovery, often involves large labeled graphs such as knowledge graphs, social networks, and protein sequences. Effective methods for extracting matching subgraphs, in terms of label and structural similarities to a query, should depict accuracy, computational efficiency, and robustness to noise. In this paper, we propose VerSaChI for finding the top-k most similar subgraphs based on 2-hop label and structural overlap similarity with the query. The similarity is characterized using Chebyshev's inequality to compute the chi-square statistical significance for measuring the degree of matching of the subgraphs. Experiments on real-life graph datasets showcase significant improvements in terms of accuracy compared to state-of-the-art methods, as well as robustness to noise.
ClaSP - Time Series Segmentation
Authors: Patrick Schäfer, Arik Ermshaus, Ulf Leser
Venue: CIKM
Year: 2021

The study of biological or physical processes often results in long sequences of temporally-ordered values, aka time series (TS). Changes in the observed processes, e.g. as a cause of natural events or internal state changes, result in changes of the measured values. Time series segmentation (TSS) tries to find such changes in TS to deduce changes in the underlying process. TSS is typically approached as an unsupervised learning problem aiming at the identification of segments distinguishable by some statistical property. We present ClaSP, a novel and highly accurate method for TSS. ClaSP hierarchically splits a TS into two parts, where each split point is determined by training a binary TS classifier for each possible split point and selecting the one with highest accuracy, i.e., the one that is best at identifying subsequences to be from either of the partitions. In our experimental evaluation using a benchmark of 98 datasets, we show that ClaSP outperforms the state-of-the-art in terms of accuracy and is also faster than the second best method. We highlight properties of ClaSP using several real-life time series.
50 Ways to Bake a Cookie: Mapping the Landscape of Procedural Texts
Authors: Moran Mizrahi, Dafna Shahaf
Venue: CIKM
Year: 2021

The web is full of guidance on a wide variety of tasks, from changing the oil in your car to baking an apple pie. However, as content is created independently, a single task could have thousands of corresponding procedural texts. This makes it difficult for users to view the bigger picture and understand the multiple ways the task could be accomplished. In this work we propose an unsupervised learning approach for summarizing multiple procedural texts into an intuitive graph representation, allowing users to easily explore commonalities and differences. We demonstrate our approach on recipes, a prominent example of procedural texts. User studies show that our representation is intuitive and coherent and that it has the potential to help users with several sensemaking tasks, including adapting recipes for a novice cook and finding creative ways to spice up a dish.
Structural Deep Incomplete Multi-view Clustering Network
Authors: Jie Wen, Zhihao Wu, Zheng Zhang, Lunke Fei, Bob Zhang, Yong Xu
Venue: CIKM
Year: 2021

In recent years, incomplete multi-view clustering has drawn increasing attention due to the existence of large amounts of unlabeled incomplete data whose views are not fully observed in the practical applications. Although many traditional methods have been extended to address the incomplete learning problem, most of them exploit the shallow models and ignore the geometric structure. To address these issues, we proposed a structural deep incomplete multi-view clustering network. Specifically, the proposed method can simultaneously explore the high-level features and high-order geometric structure information of data with several view-specific graph convolutional encoder networks and can directly obtain the optimal clustering indicator matrix in one stage. Experimental results on several datasets with the comparison of state-of-the-art methods validate the superiority of the proposed method.
Accelerating Variant Calling on Human Genomes Using a Commodity Cluster
Authors: Praveen Rao, Arun Zachariah, Deepthi S. Rao, Peter J. Tonellato, Wesley C. Warren, Eduardo J. Simões
Venue: CIKM
Year: 2021

Variant calling is a fundamental task that is performed to identify variants in an individual's genome compared to a reference human genome. This task can enable better understanding of an individual's risk to diseases and eventually lead to new innovations in precision medicine and drug discovery. However, variant calling on a large number of human genome sequences requires significant computing and storage resources. While access to such resources is possible today (e.g., through cloud computing), reducing the cost of analyzing genomes has become a major challenge. Motivated by these reasons, we address the problem of accelerating the variant calling pipeline on a large number of human genome sequences using a commodity cluster. We propose a novel approach that synergistically combines data and task parallelism for different stages of the variant calling pipeline across different sequences with minimal synchronization. Our approach employs futures to enable asynchronous computations in order to improve the overall cluster utilization and thereby, accelerate the variant calling pipeline. On a 16-node cluster, we observed that our approach was 3X-4.7X faster than the state-of-the-art Big Data Genomics software.
Tabular Functional Block Detection with Embedding-based Agglomerative Cell Clustering
Authors: Kexuan Sun, Fei Wang, Muhao Chen, Jay Pujara
Venue: CIKM
Year: 2021

Tables are a widely-used format for data curation. The diversity of domains, layouts, and content of tables makes knowledge extraction challenging. Understanding table layouts is an important step for automatically harvesting knowledge from tabular data. Since table cells are spatially organized into regions, correctly identifying such regions and inferring their functional roles, referred to as functional block detection, is a critical part of understanding table layouts. Earlier functional block detection approaches fail to leverage spatial relationships and higher-level structure, either depending on cell-level predictions or relying on data types as signals for identifying blocks. In this paper, we introduce a flexible functional block detection method by applying agglomerative clustering techniques which merge smaller blocks into larger blocks using two merging strategies. Our proposed method uses cell embeddings with a customized dissimilarity function which utilizes local and margin distances, as well as block coherence metrics to capture cell, block, and table scoped features. Given the diversity of tables in real-world corpora, we also introduce a sampling-based approach for automatically tuning distance thresholds for each table. Experimental results show that our method improves over the earlier state-of-the-art method in terms of several evaluation metrics.
WebKE: Knowledge Extraction from Semi-structured Web with Pre-trained Markup Language Model
Authors: Chenhao Xie, Wenhao Huang, Jiaqing Liang, Chengsong Huang, Yanghua Xiao
Venue: CIKM
Year: 2021

The World Wide Web contains rich up-to-date information for knowledge graph construction. However, most current relation extraction techniques are designed for free text and thus do not handle well semi-structured web content. In this paper, we propose a novel multi-phase machine reading framework, called WebKE. It processes the web content on different granularity by first detecting areas of interest at DOM tree node level and then extracting relational triples for each area. We also propose HTMLBERT as an encoder the web content. It is a pre-trained markup language model that fully leverages the visual layout information and DOM-tree structure, without the need of hand engineered features. Experimental results show that the proposed approach outperforms state-of- the-art methods by a considerable gain. The source code is available at https://github.com/redreamality/webke.
LT-OCF: Learnable-Time ODE-based Collaborative Filtering
Authors: Jeongwhan Choi, Jinsung Jeon, Noseong Park
Venue: CIKM
Year: 2021

Collaborative filtering (CF) is a long-standing problem of recommender systems. Many novel methods have been proposed, ranging from classical matrix factorization to recent graph convolutional network-based approaches. After recent fierce debates, researchers started to focus on linear graph convolutional networks (GCNs) with a layer combination, which show state-of-the-art accuracy in many datasets. In this work, we extend them based on neural ordinary differential equations (NODEs), because the linear GCN concept can be interpreted as a differential equation, and present the method of Learnable-Time ODE-based Collaborative Filtering (LT-OCF). The main novelty in our method is that after redesigning linear GCNs on top of the NODE regime, i) we learn the optimal architecture rather than relying on manually designed ones, ii) we learn smooth ODE solutions that are considered suitable for CF, and iii) we test with various ODE solvers that internally build a diverse set of neural network connections. We also present a novel training method specialized to our method. In our experiments with three benchmark datasets, our method consistently outperforms existing methods in terms of various evaluation metrics. One more important discovery is that our best accuracy was achieved by dense connections.
CIKM 2021 Tutorial on Fairness of Machine Learning in Recommender Systems
Authors: Yunqi Li, Yingqiang Ge, Yongfeng Zhang
Venue: CIKM
Year: 2021

Recently, there has been growing attention on fairness considerations in machine learning. As one of the most pervasive applications of machine learning, recommender systems are gaining increasing and critical impacts on human and society since a growing number of users use them for information seeking and decision making. Therefore, it is crucial to address the potential unfairness problems in recommendation, which may hurt users' or providers' satisfaction in recommender systems as well as the interests of the platforms. The tutorial focuses on the foundations and algorithms for fairness in recommendation. It also presents a brief introduction about fairness in basic machine learning tasks such as classification and ranking. The tutorial will introduce the taxonomies of current fairness definitions and evaluation metrics for fairness concerns. We will introduce previous works about fairness in recommendation and also put forward future fairness research directions. The tutorial aims at introducing and communicating fairness in recommendation methods to the community, as well as gathering researchers and practitioners interested in this research direction for discussions, idea communications, and research promotions.
Fair and Robust Classification Under Sample Selection Bias
Authors: Wei Du, Xintao Wu
Venue: CIKM
Year: 2021

To address the sample selection bias between the training and test data, previous research works focus on reweighing biased training data to match the test data and then building classification models on the reweighed training data. However, how to achieve fairness in the built classification models is under-explored. In this paper, we propose a framework for robust and fair learning under sample selection bias. Our framework adopts the reweighing estimation approach for bias correction and the minimax robust estimation approach for achieving robustness on prediction accuracy. Moreover, during the minimax optimization, the fairness is achieved under the worst case, which guarantees the model's fairness on test data. We further develop two algorithms to handle sample selection bias when test data is both available and unavailable.
Speedup Robust Graph Structure Learning with Low-Rank Information
Authors: Hui Xu, Liyao Xiang, Jiahao Yu, Anqi Cao, Xinbing Wang
Venue: CIKM
Year: 2021

Recent studies have shown that graph neural networks (GNNs) are vulnerable to unnoticeable adversarial perturbations, which largely confines their deployment in many safety-critical domains. Robust graph structure learning has been proposed to improve the GNN performance in the face of adversarial attacks. In particular, the low-rank methods are utilized to purify the perturbed graphs. However, these methods are mostly computationally expensive with O(n3) time complexity and O(n2) space complexity. We propose LRGNN, a fast and robust graph structure learning framework, which exploits the low-rank property as prior knowledge to speed up optimization. To eliminate adversarial perturbation, LRGNN decouples the adjacency matrix into a low-rank component and a sparse one, and learns by minimizing the rank of the first part while suppressing the second part. Its sparse variant is formed to reduce the memory footprint further. Experimental results on various attack settings have shown LRGNN acquires comparable robustness with the state-of-the-art much more efficiently, boasting a significant advantage on large-scale graphs.
Discovering Time-invariant Causal Structure from Temporal Data
Authors: Saima Absar, Lu Zhang
Venue: CIKM
Year: 2021

Discovering causal structure from temporal data is an important problem in many fields in science. Existing methods usually suffer from several limitations such as assuming linear dependencies among features, limiting to discrete time series, and/or assuming stationarity, i.e., causal dependencies are repeated with the same time lag and strength at all time points. In this paper, we propose an algorithm called the μ-PC that addresses these limitations. It is based on the theory of μ-separation and extends the well-known PC algorithm to the time domain. To be applicable to both discrete and continuous time series, we develop a conditional independence testing technique for time series by leveraging the Recurrent Marked Temporal Point Process (RMTPP) model. Experiments using both synthetic and real-world datasets demonstrate the effectiveness of the proposed algorithm.
Goal-Directed Extractive Summarization of Financial Reports
Authors: Yash Agrawal, Vivek Anand, Manish Gupta, S. Arunachalam, Vasudeva Varma
Venue: CIKM
Year: 2021

Financial reports filed by various companies discuss compliance, risks, and future plans, such as goals and new projects, which directly impact their stock price. Quick consumption of such information is critical for financial analysts and investors to make stock buy/sell decisions and for equity evaluations. Hence, we study the problem of extractive summarization of 10-K reports. Recently, Transformer-based summarization models have become very popular. However, lack of in-domain labeled summarization data is a major roadblock to train such finance-specific summarization models. We also show that zero-shot inference on such pretrained models is not as effective either. In this paper, we address this challenge by modeling 10-K report summarization using a goal-directed setting where we leverage summaries with labeled goal-related data for the stock buy/sell classification goal. Further, we provide improvements by considering a multi-task learning method with an industry classification auxiliary task. Intrinsic evaluation as well as extrinsic evaluation for the stock buy/sell classification and portfolio construction tasks shows that our proposed method significantly outperforms strong baselines.