OpenRaiser/Intern-Atlas huggingface dataset:
Semi-supervised ranking aggregation
Authors: Shouchun Chen, Fei Wang, Yangqiu Song, Changshui Zhang
Venue: Conference CIKM 2008
Year: 2008

Ranking aggregation is important in data mining and information retrieval. In this paper, we proposed a semi-supervised ranking aggregation method, in which the order of several item pairs are labeled as side information. The core idea is to learn a ranking function based on the ordering agreement of different rankers. The ranking scores assigned by this ranking function on the labeled data are consistent with the given pairwise order constraints while the ranking scores on the unlabeled data obey the intrinsic manifold structure of the rank items. The experiment results show our method work well.
Supervised rank aggregation
Authors: Yuting Liu, Tie-Yan Liu, Tao Qin, Zhiming Ma, Hang Li
Venue: Conference WWW 2007
Year: 2007

This paper is concerned with rank aggregation, the task of combining the ranking results of individual rankers at meta-search. Previously, rank aggregation was performed mainly by means of unsupervised learning. To further enhance ranking accuracies, we propose employing supervised learning to perform the task, using labeled data. We refer to the approach as Supervised Rank Aggregation. We set up a general framework for conducting Supervised Rank Aggregation, in which learning is formalized an optimization which minimizes disagreements between ranking results and the labeled data. As case study, we focus on Markov Chain based rank aggregation in this paper. The optimization for Markov Chain based methods is not a convex optimization problem, however, and thus is hard to solve. We prove that we can transform the optimization problem into that of Semidefinite Programming and solve it efficiently. Experimental results on meta-searches show that Supervised Rank Aggregation can significantly outperform existing unsupervised methods.
Supervised rank aggregation approach for link prediction in complex networks
Authors: Manisha Pujari, Rushed Kanawati
Venue: Conference WWW 2012
Year: 2012

In this paper we propose a new topological approach for link prediction in dynamic complex networks. The proposed approach applies a supervised rank aggregation method. This functions as follows: first we rank the list of unlinked nodes in a network at instant t according to different topological measures (nodes characteristics aggregation, nodes neighborhood based measures, distance based measures, etc). Each measure provides its own rank. Observing the network at instant t+1 where some new links appear, we weight each topological measure according to its performances in predicting these observed new links. These learned weights are then used in a modified version of classical computational social choice algorithms (such as Borda, Kemeny, etc) in order to have a model for predicting new links. We show the effectiveness of this approach through different experimentations applied to co-authorship networks extracted from the DBLP bibliographical database. Results we obtain, are also compared with the outcome of classical supervised machine learning based link prediction approaches applied to the same datasets.
A soft computing approach for learning to aggregate rankings
Authors: Javier Alvaro Vargas Muñoz, Ricardo da Silva Torres, Marcos André Gonçalves
Venue: Conference CIKM 2015
Year: 2015

This paper presents an approach to combine rank aggregation techniques using a soft computing technique -- Genetic Programming -- in order to improve the results in Information Retrieval tasks. Previous work shows that by combining rank aggregation techniques in an agglomerative way, it is possible to get better results than with individual methods. However, these works either combine only a small set of lists or are performed in a completely ad-hoc way. Therefore, given a set of ranked lists and a set of rank aggregation techniques, we propose to use a supervised genetic programming approach to search combinations of them that maximize effectiveness in large search spaces. Experimental results conducted using four datasets with different properties show that our proposed approach reaches top performance in most datasets. Moreover, this cross-dataset performance is not matched by any other baseline among the many we experiment with, some being the state-of-the-art in learning-to-rank and in the supervised rank aggregation tasks. We also show that our proposed framework is very efficient, flexible, and scalable.
Autoqgs: Auto-prompt for low-resource knowledge-based question generation from sparql
Authors: Guanming Xiong, Junwei Bao, Wen Zhao, Youzheng Wu, Xiaodong He
Venue: Conference CIKM 2022
Year: 2022

This study investigates the task of knowledge-based question generation (KBQG). Conventional KBQG works generated questions from fact triples in the knowledge graph, which could not express complex operations like aggregation and comparison in SPARQL. Moreover, due to the costly annotation of large-scale SPARQL-question pairs, KBQG from SPARQL under low-resource scenarios urgently needs to be explored. Recently, since the generative pre-trained language models (PLMs) typically trained in natural language (NL)-to-NL paradigm have been proven effective for low-resource generation, e.g., T5 and BART, how to effectively utilize them to generate NL-question from non-NL SPARQL is challenging. To address these challenges, AutoQGS, an auto-prompt approach for low-resource KBQG from SPARQL, is proposed. Firstly, we put forward to generate questions directly from SPARQL for the KBQG task to handle complex operations. Secondly, we propose an auto-prompter trained on large-scale unsupervised data to rephrase SPARQL into NL description, smoothing the low-resource transformation from non-NL SPARQL to NL question with PLMs. Experimental results on the WebQuestionsSP, ComlexWebQuestions 1.1, and PathQuestions show that our model achieves state-of-the-art performance, especially in low-resource settings. Furthermore, a corpus of 330k factoid complex question-SPARQL pairs is generated for further KBQG research.
Identifying cost-effective debunkers for multi-stage fake news mitigation campaigns
Authors: Xiaofei Xu, Ke Deng, Xiuzhen Zhang
Venue: Conference WSDM 2022
Year: 2022

Online social networks have become a fertile ground for spreading fake news. Methods to automatically mitigate fake news propagation have been proposed. Some studies focus on selecting top k influential users on social networks as debunkers, but the social influence of debunkers may not translate to wide mitigation information propagation as expected. Other studies assume a given set of debunkers and focus on optimizing intensity for debunkers to publish true news, but as debunkers are fixed, even if with high social influence and/or high intensity to post true news, the true news may not reach users exposed to fake news and therefore mitigation effect may be limited. In this paper, we propose the multi-stage fake news mitigation campaign where debunkers are dynamically selected within budget at each stage. We formulate it as a reinforcement learning problem and propose a greedy algorithm optimized by predicting future states so that the debunkers can be selected in a way that maximizes the overall mitigation effect. We conducted extensive experiments on synthetic and real-world social networks and show that our solution outperforms state-of-the-art baselines in terms of mitigation effect.
Mtsci: A conditional diffusion model for multivariate time series consistent imputation
Authors: Jianping Zhou, Junhao Li, Guanjie Zheng, Xinbing Wang, Chenghu Zhou
Venue: Conference CIKM 2024
Year: 2024

Missing values are prevalent in multivariate time series, compromising the integrity of analyses and degrading the performance of downstream tasks. Consequently, research has focused on multivariate time series imputation, aiming to accurately impute the missing values based on available observations. A key research question is how to ensure imputation consistency, i.e., intra-consistency between observed and imputed values, and inter-consistency between adjacent windows after imputation. However, previous methods rely solely on the inductive bias of the imputation targets to guide the learning process, ignoring imputation consistency and ultimately resulting in poor performance. Diffusion models, known for their powerful generative abilities, prefer to generate consistent results based on available observations. Therefore, we propose a conditional diffusion model for Multivariate Time Series Consistent Imputation (MTSCI). Specifically, MTSCI employs a contrastive complementary mask to generate dual views during the forward noising process. Then, the intra contrastive loss is calculated to ensure intra-consistency between the imputed and observed values. Meanwhile, MTSCI utilizes a mixup mechanism to incorporate conditional information from adjacent windows during the denoising process, facilitating the inter-consistency between imputed samples. Extensive experiments on multiple real-world datasets demonstrate that our method achieves the state-of-the-art performance on multivariate time series imputation task under different missing scenarios. Code is available at https://github.com/JeremyChou28/MTSCI.
Modeling dual period-varying preferences for takeaway recommendation
Authors: Yuting Zhang 0010, Yiqing Wu, Ran Le, Yongchun Zhu, Fuzhen Zhuang, Ruidong Han, Xiang Li 0067, Wei Lin 0022, Zhulin An, Yongjun Xu 0001
Venue: Conference KDD 2023
Year: 2023

Takeaway recommender systems, which aim to accurately provide stores that offer foods meeting users' interests, have served billions of users in our daily life. Different from traditional recommendation, takeaway recommendation faces two main challenges: (1) Dual Interaction-Aware Preference Modeling. Traditional recommendation commonly focuses on users' single preferences for items while takeaway recommendation needs to comprehensively consider users' dual preferences for stores and foods. (2) Period-Varying Preference Modeling. Conventional recommendation generally models continuous changes in users' preferences from a session-level or day-level perspective. However, in practical takeaway systems, users' preferences vary significantly during the morning, noon, night, and late night periods of the day. To address these challenges, we propose a Dual Period-Varying Preference modeling (DPVP) for takeaway recommendation. Specifically, we design a dual interaction-aware module, aiming to capture users' dual preferences based on their interactions with stores and foods. Moreover, to model various preferences in different time periods of the day, we propose a time-based decomposition module as well as a time-aware gating mechanism. Extensive offline and online experiments demonstrate that our model outperforms state-of-the-art methods on real-world datasets and it is capable of modeling the dual period-varying preferences. Moreover, our model has been deployed online on Meituan Takeaway platform, leading to an average improvement in GMV (Gross Merchandise Value) of 0.70%.
Fairness in streaming submodular maximization subject to a knapsack constraint
Authors: Shuang Cui, Kai Han 0003, Shaojie Tang 0001, Feng Li 0002, Jun Luo 0001
Venue: Conference KDD 2024
Year: 2024

Submodular optimization has been identified as a powerful tool for many data mining applications, where a representative subset of moderate size needs to be extracted from a large-scale dataset. In scenarios where data points possess sensitive attributes such as age, gender, or race, it becomes imperative to integrate fairness measures into submodular optimization to mitigate bias and discrimination. In this paper, we study the fundamental problem of fair submodular maximization subject to a knapsack constraint and propose the first streaming algorithm for it with provable performance guarantees for both monotone and non-monotone submodular functions. As a byproduct, we also propose a streaming algorithm for submodular maximization subject to a partition matroid and a knapsack constraint, significantly improving the performance bounds achieved by previous work. We conduct extensive experiments on real-world applications such as movie recommendation, image summarization, and maximum coverage in social networks. The experimental results strongly demonstrate the superiority of our proposed algorithms in terms of both fairness and utility.
Enabling privacy-preserving sharing of genomic data for gwass in decentralized networks
Authors: Yanjun Zhang, Xin Zhao, Xue Li, Mingyang Zhong, Caitlin Curtis, Chen Chen
Venue: Conference WSDM 2019
Year: 2019

The human genome can reveal sensitive information and is potentially re-identifiable, which raises privacy and security concerns about sharing such data on wide scales. In this work, we propose a preventive approach for privacy-preserving sharing of genomic data in decentralized networks for Genome-wide association studies (GWASs), which have been widely used in discovering the association between genotypes and phenotypes. The key components of this work are: a decentralized secure network, with a privacy- preserving sharing protocol, and a gene fragmentation framework that is trainable in an end-to-end manner. Our experiments on real datasets show the effectiveness of our privacy-preserving approaches as well as significant improvements in efficiency when compared with recent, related algorithms.
Unpaired Generative Molecule-to-Molecule Translation for Lead Optimization
Authors: Guy Barshatski, Kira Radinsky
Venue: Conference KDD 2021
Year: 2021

Molecular lead optimization is an important task of drug discovery focusing on generating novel molecules similar to a drug candidate but with enhanced properties. Prior works focused on supervised models requiring datasets of pairs of a molecule and an enhanced molecule. These approaches require large amounts of data and are limited by the bias of the specific examples of enhanced molecules. In this work, we present an unsupervised generative approach with a molecule-embedding component that maps a discrete representation of a molecule to a continuous space. The components are then coupled with a unique training architecture leveraging molecule fingerprints and applying double cycle constraints to enable both chemical resemblance to the original molecular lead while generating novel molecules with enhanced properties. We evaluate our method on multiple common molecular optimization tasks, including dopamine receptor (DRD2) and drug likeness (QED), and show our method outperforms previous state-of-the-art baselines. Moreover, we conduct thorough ablation experiments to show the effect and necessity of important components in our model. Furthermore, we demonstrate our method's ability to generate FDA-approved drugs it has never encountered before, such as Perazine and Clozapine, which are used to treat psychotic disorders, like Schizophrenia. The system is currently being deployed for use in the Targeted Drug Delivery and Personalized Medicine laboratories generating treatments using nanoparticle-based technology.
Multi-property molecular optimization using an integrated poly-cycle architecture
Authors: Guy Barshatski, Galia Nordon, Kira Radinsky
Venue: Conference CIKM 2021
Year: 2021

Molecular lead optimization is an important task of drug discovery focusing on generating molecules similar to a drug candidate but with enhanced properties. Most prior works focused on optimizing a single property. However, in real settings, we wish to find molecules that satisfy multiple constraints, e.g., potency and safety. Simultaneously optimizing these constraints was shown to be difficult, mostly due to the lack of training examples satisfying all constraints. In this work, we present a novel approach for multi-property optimization. Unlike prior approaches, that require a large training set of pairs of a lead molecule and an enhanced molecule, our approach is unpaired. Our architecture learns a transformation for each property optimization separately, while constraining the latent embedding space between all transformations. This allows generating a molecule which optimizes multiple properties simultaneously. We present a novel adaptive loss which balances the separate transformations and stabilizes the optimization process. We evaluate our method on optimizing for two properties: dopamine receptor (DRD2) and drug likeness (QED), and show our method outperforms previous state-of-the-art, especially when training examples satisfying all constraints are sparse.
Adversaries with limited information in the friedkin-johnsen model
Authors: Sijing Tu, Stefan Neumann 0003, Aristides Gionis
Venue: Conference KDD 2023
Year: 2023

In recent years, online social networks have been the target of adversaries who seek to introduce discord into societies, to undermine democracies and to destabilize communities. Often the goal is not to favor a certain side of a conflict but to increase disagreement and polarization. To get a mathematical understanding of such attacks, researchers use opinion-formation models from sociology, such as the Friedkin--Johnsen model, and formally study how much discord the adversary can produce when altering the opinions for only a small set of users. In this line of work, it is commonly assumed that the adversary has full knowledge about the network topology and the opinions of all users. However, the latter assumption is often unrealistic in practice, where user opinions are not available or simply difficult to estimate accurately. To address this concern, we raise the following question: Can an attacker sow discord in a social network, even when only the network topology is known? We answer this question affirmatively. We present approximation algorithms for detecting a small set of users who are highly influential for the disagreement and polarization in the network. We show that when the adversary radicalizes these users and if the initial disagreement/polarization in the network is not very high, then our method gives a constant-factor approximation on the setting when the user opinions are known. To find the set of influential users, we provide a novel approximation algorithm for a variant of MaxCut in graphs with positive and negative edge weights. We experimentally evaluate our methods, which have access only to the network topology, and we find that they have similar performance as methods that have access to the network topology and all user opinions. We further present an NP-hardness proof, which was an open question by Chen and Racz [IEEE Trans. Netw. Sci. Eng., 2021].
Spatio-temporal trajectory similarity learning in road networks
Authors: Ziquan Fang, Yuntao Du 0002, Xinjun Zhu, Danlei Hu, Lu Chen 0001, Yunjun Gao, Christian S. Jensen
Venue: Conference KDD 2022
Year: 2022

Deep learning based trajectory similarity computation holds the potential for improved efficiency and adaptability over traditional similarity computation. However, existing learning-based trajectory similarity learning solutions prioritize spatial similarity over temporal similarity, making them suboptimal for time-aware analyses. To this end, we propose ST2Vec, a representation learning based solution that considers fine-grained spatial and temporal relations between trajectories to enable spatio-temporal similarity computation in road networks. Specifically, ST2Vec encompasses two steps: (i) spatial and temporal modeling that encode spatial and temporal information of trajectories, where a generic temporal modeling module is proposed for the first time; and (ii) spatio-temporal co-attention fusion, where two fusion strategies are designed to enable the generation of unified spatio-temporal embeddings of trajectories. Further, under the guidance of triplet loss, ST2Vec employs curriculum learning in model optimization to improve convergence and effectiveness. An experimental study offers evidence that ST2Vec outperforms state-of-the-art competitors substantially in terms of effectiveness and efficiency, while showing low parameter sensitivity and good model robustness. Moreover, similarity involved case studies including top-k querying and DBSCAN clustering offer further insight into the capabilities of ST2Vec.
Meta-learning for phonemic annotation of corpora
Authors: Véronique Hoste, Walter Daelemans, Erik F. Tjong Kim Sang, Steven Gillis
Venue: Conference ICML 2000
Year: 2000

We apply rule induction, classifier combination and meta-learning (stacked classifiers) to the problem of bootstrapping high accuracy automatic annotation of corpora with pronunciation information. The task we address in this paper consists of generating phonemic representations reflecting the Flemish and Dutch pronunciations of a word on the basis of its orthographic representation (which in turn is based on the actual speech recordings). We compare several possible approaches to achieve the text-to-pronunciation mapping task: memory-based learning, transformation-based learning, rule induction, maximum entropy modeling, combination of classifiers in stacked learning, and stacking of meta-learners. We are interested both in optimal accuracy and in obtaining insight into the linguistic regularities involved. As far as accuracy is concerned, an already high accuracy level (93% for Celex and 86% for Fonilex at word level) for single classifiers is boosted significantly with additional error reductions of 31% and 38% respectively using combination of classifiers, and a further 5% using combination of meta-learners, bringing overall word level accuracy to 96% for the Dutch variant and 92% for the Flemish variant. We also show that the application of machine learning methods indeed leads to increased insight into the linguistic regularities determining the variation between the two pronunciation variants studied.
On the complexity of probabilistic Image Retrieval
Authors: Nuno Vasconcelos
Venue: Conference ICCV 2001
Year: 2001

Probabilistic image retrieval approaches can lead to significant gains over standard retrieval techniques. However, this occurs at the cost of a significant increase in computational complexity. In fact, closed-form solutions for probabilistic retrieval are currently available only for simple representations such as the Gaussian and the histogram. We analyze the case of mixture densities and exploit the asymptotic equivalence between likelihood and Kullback-Leibler divergence to derive solutions for these models. In particular, (1) we show that the divergence can be computed exactly for vector quantizers and, (2) has an approximate solution for Gaussian mixtures that introduces no significant degradation of the resulting similarity judgments. In both cases, the new solutions have closed-form and computational complexity equivalent to that of standard retrieval approaches, but significantly better retrieval performance.
Inferring Social Relations from Visual Concepts
Authors: Lei Ding 0002, Alper Yilmaz 0001
Venue: Conference ICCV 2011
Year: 2011

In this paper, we study the problem of social relational inference using visual concepts which serve as indicators of actors' social interactions. While social network analysis from videos has started to gain attention in the recent years, the existing work either uses proximity or co-occurrence statistics, or exploit a holistic model of the scene content where the relations are assumed to stay constant throughout the video. This work permits changing relations and argues that there exists a relationship between the visual concepts and the social relations among actors, which is a fundamentally new concept in computer vision. Specifically, we leverage the existing large-scale concept detectors to generate concept score vectors to represent the video content, and we further map them to grouping cues that are used to detect the social structure. In our framework, a probabilistic graphical model with temporal smoothing provides a means to analyze social relations among actors and detect communities. Experiments on Youtube videos and theatrical movies validate the proposed framework.