OpenRaiser/Intern-Atlas huggingface dataset:
Mining anchor text for query refinement
Authors: Reiner Kraft, Jason Y. Zien
Venue: Conference WWW 2004
Year: 2004

When searching large hypertext document collections, it is often possible that there are too many results available for ambiguous queries. Query refinement is an interactive process of query modification that can be used to narrow down the scope of search results. We propose a new method for automatically generating refinements or related terms to queries by mining anchor text for a large hypertext document collection. We show that the usage of anchor text as a basis for query refinement produces high quality refinement suggestions that are significantly better in terms of perceived usefulness compared to refinements that are derived using the document content. Furthermore, our study suggests that anchor text refinements can also be used to augment traditional query refinement algorithms based on query logs, since they typically differ in coverage and produce different refinements. Our results are based on experiments on an anchor text collection of a large corporate intranet.
Consistency and Variation in Kernel Neural Ranking Model
Authors: Mary Arpita Pyreddy, Varshini Ramaseshan, Narendra Nath Joshi, Zhuyun Dai, Chenyan Xiong, Jamie Callan, Zhiyuan Liu
Venue: Conference SIGIR 2018
Year: 2018

This paper studies the consistency of the kernel-based neural ranking model K-NRM, a recent state-of-the-art neural IR model, which is important for reproducible research and deployment in the industry. We find that K-NRM has low variance on relevance-based metrics across experimental trials. In spite of this low variance in overall performance, different trials produce different document rankings for individual queries. The main source of variance in our experiments was found to be different latent matching patterns captured by K-NRM. In the IR-customized word embeddings learned by K-NRM, the query-document word pairs follow two different matching patterns that are equally effective, but align word pairs differently in the embedding space. The different latent matching patterns enable a simple yet effective approach to construct ensemble rankers, which improve K-NRM's effectiveness and generalization abilities.
Applying a multi-level modeling theory to assess taxonomic hierarchies in Wikidata
Authors: Freddy Brasileiro, João Paulo A. Almeida, Victorio Albani de Carvalho, Giancarlo Guizzardi
Venue: Conference WWW 2016
Year: 2016

Wikidata captures structured data on a number of subject domains, managing, among others, the information underlying Wikipedia and other Wikimedia projects. Wikidata serves as a repository of structured data, whose purpose is to support the consistent sharing and linking of data on the Web. To support these purposes, it is key that Wikidata is built on consistent data models and representation schemas, which are constructed and managed in a collaborative platform. In this paper, we address the quality of taxonomic hierarchies in Wikidata. We focus on taxonomic hierarchies with entities at different classification levels (particular individuals, types of individuals, types of types of individuals, etc.). We use an axiomatic theory for multi-level modeling to analyze current Wikidata content, and identify a significant number of problematic classification and taxonomic statements. The problems seem to arise from an inadequate use of instantiation and subclassing in certain Wikidata hierarchies.
What is trending on Wikipedia? capturing trends and language biases across Wikipedia editions
Authors: Volodymyr Miz, Joëlle Hanna, Nicolas Aspert, Benjamin Ricaud, Pierre Vandergheynst
Venue: Conference WWW 2020
Year: 2020

In this work, we propose an automatic evaluation and comparison of the browsing behavior of Wikipedia readers that can be applied to any language editions of Wikipedia. As an example, we focus on English, French, and Russian languages during the last four months of 2018. The proposed method has three steps. Firstly, it extracts the most trending articles over a chosen period of time. Secondly, it performs a semi-supervised topic extraction and thirdly, it compares topics across languages. The automated processing works with the data that combines Wikipedia’s graph of hyperlinks, pageview statistics and summaries of the pages. The results show that people share a common interest and curiosity for entertainment, e.g. movies, music, sports independently of their language. Differences appear in topics related to local events or about cultural particularities. Interactive visualizations showing clusters of trending pages in each language edition are available online https://wiki-insights.epfl.ch/wikitrendshttps://wiki-insights.epfl.ch/wikitrends
Local Collaborative Autoencoders
Authors: Minjin Choi, Yoonki Jeong, Joonseok Lee, Jongwuk Lee
Venue: Conference WSDM 2021
Year: 2021

This work presents a generalized local factor model, namely Local Collaborative Autoencoders (LOCA). To our knowledge, it is the first generalized framework under the local low-rank assumption that builds on the neural recommendation models. We explore a large number of local models by adopting a generalized framework with different weight schemes for training and aggregating them. Besides, we develop a novel method of discovering a sub-community to maximize the coverage of local models. Our experimental results demonstrate that LOCA is highly scalable, achieving state-of-the-art results by outperforming existing AE-based and local latent factor models on several large-scale public benchmarks.
On Twitter Purge: A Retrospective Analysis of Suspended Users
Authors: Farhan Asif Chowdhury, Lawrence Allen, Mohammad Yousuf, Abdullah Mueen
Venue: Conference WWW 2020
Year: 2020

Abuse and spam in Twitter have long been a pressing issue, and in response, Twitter regularly purges (i.e., suspends in mass) accounts that violate Twitter Rules. However, there is no available information about the characteristics and activities of these regularly purged users. We have developed a novel and comprehensive measurement mechanism to identify millions of purged Twitter users and collect their tweets. We have identified 2.4M purged users and collected 1M tweets made by them over eight months. Using our dataset, we perform a retrospective analysis to characterize their account properties and behavioral activities. We analyze their tweet content to identify their role and abuse strategy over-time. Our analysis shows that the abuse on Twitter is pervasive globally and not confined in mere spamming. Alarmingly, more than 60% of the purged users survived on Twitter for more than two years. We observe that politics is a major theme among the purged users irrespective of language and location, and these politically motivated users spread controversial content consistently over time. However, the spammers reorient their agenda across time to participate in multiple marketing campaigns. We also discover interaction and associated communities among purged users. Our analysis sheds new light on the evolving nature of abuse in Twitter that can help researchers understanding the characteristics and behavior of emerging malicious users to develop an effective defense system.
User Stance Prediction via Online Behavior Mining
Authors: Yizhou Sun
Venue: Conference WWW 2017
Year: 2017

Nowadays social media, such as Twitter and all kinds of online forums, becomes a platform where people can express their opinions implicitly or explicitly. For example, in Twitter, people follow people they trust, and retweet the tweets they agree. In online forums, such as PoliticalForum.com, people explicitly express their opinions and interact with each other using text. Our goal is to understand people's stance on some (political) issue according to their online behaviors that can be captured in social media, including links they have issued and content they have generated, which is essential for national security and policy making. Existing attempts in this direction, however, oversimplified the problem in several aspects. First, they usually treat the user stance prediction problem as a binary classification problem, e.g., left or right, or positive or negative, while the extent of people's attitude is very critical. Second, most of the existing work depends heavily on labels, which is unacceptable for large-scale social media data and impossible to label when user stance is modeled as a numerical number. Third, most of the methods do not attempt to understand the rationality behind their online behaviors. In contrast, (1) our proposed methods can predict user stance in terms of numerical values; (2) our methods are unsupervised methods and no labels are required for the analysis; and (3) the models are carefully designed with the consideration of human rationality of their choices. In particular, two specific user stance prediction problems will be included in this keynote: (1) political ideology detection for ordinary twitter users via their heterogeneous types of links; and (2) user stance prediction in news commenting system. These methodologies may benefit more applications ranging across a wide spectrum of domains.
Mell: effective embedding method for multiplex networks
Authors: Ryuta Matsuno, Tsuyoshi Murata
Venue: Conference WWW 2018
Year: 2018

Network embedding is a method for converting nodes in a network into low dimensional vectors, preserving its structure and the similarities among the nodes. Embedding is widely used in many applications, e.g., social network analysis and knowledge discovery. Because of its wide usage, many studies have been proposed, such as DeepWalk, LINE and node2vec. These works are designed for single-layer networks, however, real world networks often possess not just one, but multiple types of connections. Hence it is more appropriate to represent them as multiplex networks, which consist of multiple layers each of which represents one type of relationship. Embedding multiplex networks is difficult because all layer structures have to be taken into consideration. In this paper, we propose MELL, a novel embedding method for multiplex networks, which incorporates an idea of layer vector that captures and characterizes each layer's connectivity. This method exploits the overall structure effectively, and embeds both directed and undirected multiplex networks, whether their layer structures are similar or complementary. We focus on link prediction tasks and test our method and other baseline methods using five data sets from different domains. The results show that our method outperforms all of the baseline methods for all of the data sets.
Massively Parallel Expectation Maximization Using Graphics Processing Units
Authors: Muzaffer Can Altinigneli, Claudia Plant, Christian Böhm
Venue: Conference KDD 2013
Year: 2013

Composed of several hundreds of processors, the Graphics Processing Unit (GPU) has become a very interesting platform for computationally demanding tasks on massive data. A special hierarchy of processors and fast memory units allow very powerful and efficient parallelization but also demands novel parallel algorithms. Expectation Maximization (EM) is a widely used technique for maximum likelihood estimation. In this paper, we propose an innovative EM clustering algorithm particularly suited for the GPU platform on NVIDIA's Fermi architecture. The central idea of our algorithm is to allow the parallel threads exchanging their local information in an asynchronous way and thus updating their cluster representatives on demand by a technique called Asynchronous Model Updates (Async-EM). Async-EM enables our algorithm not only to accelerate convergence but also to reduce the overhead induced by memory bandwidth limitations and synchronization requirements. We demonstrate (1) how to reformulate the EM algorithm to be able to exchange information using Async-EM and (2) how to exploit the special memory and processor architecture of a modern GPU in order to share this information among threads in an optimal way. As a perspective Async-EM is not limited to EM but can be applied to a variety of algorithms.
Bbm: bayesian browsing model from petabyte-scale data
Authors: Chao Liu, Fan Guo, Christos Faloutsos
Venue: Conference KDD 2009
Year: 2009

Given a quarter of petabyte click log data, how can we estimate the relevance of each URL for a given query? In this paper, we propose the Bayesian Browsing Model (BBM), a new modeling technique with following advantages: (a) it does exact inference; (b) it is single-pass and parallelizable; (c) it is effective.
Personalized click model through collaborative filtering
Authors: Si Shen, Botao Amber Hu, Weizhu Chen, Qiang Yang
Venue: Conference WSDM 2012
Year: 2012

Click modeling aims to interpret the users' search click data in order to predict their clicking behavior. Existing models can well characterize the position bias of documents and snippets in relation to users' mainstream click behavior. Yet, current advances depict users' search actions only in a general setting by implicitly assuming that all users act in the same way, regardless of the fact that anyone, motivated with some individual interest, is more likely to click on a link than others. It is in light of this that we put forward a novel personalized click model to describe the user-oriented click preferences, which applies and extends matrix / tensor factorization from the view of collaborative filtering to connect users, queries and documents together. Our model serves as a generalized personalization framework that can be incorporated to the previously proposed click models and, in many cases, to their future extensions. Despite the sparsity of search click data, our personalized model demonstrates its advantage over the best click models previously discussed in the Web-search literature, supported by our large-scale experiments on a real dataset. A delightful bonus is the model's ability to gain insights into queries and documents through latent feature vectors, and hence to handle rare and even new query-document pairs much better than previous click models.
User-click modeling for understanding and predicting search-behavior
Authors: Yuchen Zhang, Weizhu Chen, Dong Wang, Qiang Yang
Venue: Conference KDD 2011
Year: 2011

Recent advances in search users' click modeling consider both users' search queries and click/skip behavior on documents to infer the user's perceived relevance. Most of these models, including dynamic Bayesian networks (DBN) and user browsing models (UBM), use probabilistic models to understand user click behavior based on individual queries. The user behavior is more complex when her actions to satisfy her information needs form a search session, which may include multiple queries and subsequent click behaviors on various items on search result pages. Previous research is limited to treating each query within a search session in isolation, without paying attention to their dynamic interactions with other queries in a search session. Investigating this problem, we consider the sequence of queries and their clicks in a search session as a task and propose a task-centric click model~(TCM). TCM characterizes user behavior related to a task as a collective whole. Specifically, we identify and consider two new biases in TCM as the basis for user modeling. The first indicates that users tend to express their information needs incrementally in a task, and thus perform more clicks as their needs become clearer. The other illustrates that users tend to click fresh documents that are not included in the results of previous queries. Using these biases, TCM is more accurately able to capture user search behavior. Extensive experimental results demonstrate that by considering all the task information collectively, TCM can better interpret user click behavior and achieve significant improvements in terms of ranking metrics of NDCG and perplexity.
CGTR: Convolution Graph Topology Representation for Document Ranking
Authors: Yuanyuan Qi, Jiayue Zhang, Yansong Liu, Weiran Xu, Jun Guo
Venue: Conference CIKM 2020
Year: 2020

Contextualized neural language models have gained much attention in Information Retrieval (IR) with its ability to achieve better text understanding by capturing contextual structure. However, to achieve better document understanding, it is necessary to involve global structure of a document. In this paper, we take the advantage of Graph Convolutional Networks (GCN) to model global word-relation structure of a document to improve context-aware document ranking. We propose to build a graph for a document to model the global structure. The nodes and edges of the graph are constructed from contextual embeddings. Then we apply graph convolution on the graph to learning a new representation, and this representation covers both contextual and global structure information. The experimental results show that our method outperforms the state-of-the-art contextual language models, which demonstrate that incorporating global structure is useful for improving document ranking and GCN is an effective way to achieve it.
Understanding the Practice of Security Patch Management across Multiple Branches in OSS Projects
Authors: Xin Tan, Yuan Zhang, Jiajun Cao, Kun Sun, Mi Zhang, Min Yang
Venue: Conference WWW 2022
Year: 2022

Since the users of open source software (OSS) projects may not use the latest version all the time, OSS development teams often support code maintenance for old versions through maintaining multiple stable branches. Typically, the developers create a stable branch for each old stable version, deploy security patches on the branch, and release fixed versions at regular intervals. As such, old-version applications in production environments are protected from the disclosed vulnerabilities in a long time. However, the rapidly growing number of OSS vulnerabilities has greatly strained this patch deployment model, and a critical need has arisen for the security community to understand the practice of security patch management across stable branches. In this work, we conduct a large-scale empirical study of stable branches in OSS projects and the security patches deployed on them via investigating 608 stable branches belonging to 26 popular OSS projects as well as more than 2,000 security fixes for 806 CVEs deployed on stable branches. Our study distills several important findings: (i) more than 80% affected CVE-Branch pairs are unpatched; (ii) the unpatched vulnerabilities could pose a serious security risk to applications in use, with 47.39% of them achieving a CVSS score over 7 (High or Critical Severity); and (iii) the patch porting process requires great manual efforts and takes an average of 40.46 days, significantly extending the time window for N-day vulnerability attacks. Our results reveal the worrying state of security patch management across stable branches. We hope our study can shed some light on improving the practice of patch management in OSS projects.
Bavarian: Betweenness Centrality Approximation with Variance-Aware Rademacher Averages
Authors: Cyrus Cousins, Chloe Wohlgemuth, Matteo Riondato
Venue: Conference KDD 2021
Year: 2021

We present Bavarian, a collection of sampling-based algorithms for approximating the Betweenness Centrality (BC) of all vertices in a graph. Our algorithms use Monte-Carlo Empirical Rademacher Averages (MCERAs), a concept from statistical learning theory, to efficiently compute tight bounds on the maximum deviation of the estimates from the exact values. The MCERAs provide a sample-dependent approximation guarantee much stronger than the state of the art, thanks to its use of variance-aware probabilistic tail bounds. The flexibility of the MCERA allows us to introduce a unifying framework that can be instantiated with existing sampling-based estimators of BC, thus allowing a fair comparison between them, decoupled from the sample-complexity results with which they were originally introduced. Additionally, we prove novel sample-complexity results showing that, for all estimators, the sample size sufficient to achieve a desired approximation guarantee depends on the vertex-diameter of the graph, an easy-to-bound characteristic quantity. We also show progressive-sampling algorithms and extensions to other centrality measures, such as percolation centrality. Our extensive experimental evaluation of Bavarian shows the improvement over the state-of-the art made possible by the MCERA, and it allows us to assess the different trade-offs between sample size and accuracy guarantee offered by the different estimators.
QUBE: a quick algorithm for updating betweenness centrality
Authors: Min-Joong Lee, Jungmin Lee, Jaimie Yejean Park, Ryan Hyun Choi, Chin-Wan Chung
Venue: Conference WWW 2012
Year: 2012

The betweenness centrality of a vertex in a graph is a measure for the participation of the vertex in the shortest paths in the graph. The Betweenness centrality is widely used in network analyses. Especially in a social network, the recursive computation of the betweenness centralities of vertices is performed for the community detection and finding the influential user in the network. Since a social network graph is frequently updated, it is necessary to update the betweenness centrality efficiently. When a graph is changed, the betweenness centralities of all the vertices should be recomputed from scratch using all the vertices in the graph. To the best of our knowledge, this is the first work that proposes an efficient algorithm which handles the update of the betweenness centralities of vertices in a graph. In this paper, we propose a method that efficiently reduces the search space by finding a candidate set of vertices whose betweenness centralities can be updated and computes their betweenness centeralities using candidate vertices only. As the cost of calculating the betweenness centrality mainly depends on the number of vertices to be considered, the proposed algorithm significantly reduces the cost of calculation. The proposed algorithm allows the transformation of an existing algorithm which does not consider the graph update. Experimental results on large real datasets show that the proposed algorithm speeds up the existing algorithm 2 to 2418 times depending on the dataset.
Matching Task Profiles and User Needs in Personalized Web Search
Authors: Julia Luxenburger, Shady Elbassuoni, Gerhard Weikum
Venue: Conference CIKM 2008
Year: 2008

Personalization has been deemed one of the major challenges in information retrieval with a significant potential for providing better search experience to individual users. Especially, the need for enhanced user models better capturing elements such as users' goals, tasks, and contexts has been identified. In this paper, we introduce a statistical language model for user tasks representing different granularity levels of a user profile, ranging from very specific search goals to broad topics. We propose a personalization framework that selectively matches the actual user information need with relevant past user tasks, and allows to dynamically switch the course of personalization from re-finding very precise information to biasing results to general user interests. In the extreme, our model is able to detect when the user's search and browse history is not appropriate for aiding the user in satisfying her current information quest. Instead of blindly applying personalization to all user queries, our approach refrains from undue actions in these cases, accounting for the user's desire of discovering new topics, and changing interests over time. The effectiveness of our method is demonstrated by an empirical user study.