OpenRaiser/Intern-Atlas huggingface dataset:
Temporal Modeling of Information Diffusion using MASEP: Multi-Actor Self-Exciting Processes
Authors: Bowen Zhang, Wing Cheong Lau
Venue: WWW
Year: 2018

Predicting the popularity of a discussion topic in an online social network (OSN) or the responses to an online fund-raising campaign is a practical challenge of immense value. Previous work tries to predict the popularity of an online campaign by modeling information diffusion as a homogeneous temporal point process within a network of a single-type of actors. However, real-world information propagation often involved multiple types of actors. In particular, there are the so-called opinion leaders, e.g. online celebrities or influential OSN users with a huge number of followers, who can create a great impact on the visibility and thus the final popularity of an event by simply mentioning it in their tweets or postings. In this paper, we propose MASEP, a Multi-actor Self-exciting Process, to model and predict the popularity of different online campaigns involving multiple types of actors. MASEP combines a self-exciting branching process with a periodical decay process to capture the dynamics and interdependent relationship between opinion leaders and ordinary users during an online campaign. A closed-form expression is derived for the temporal campaign popularity under the MASEP model. Based on this closed-form expression, we can efficiently perform regression against the empirical activity measurements of an online campaign during its early stage to estimate the parameters of the corresponding MASEP model. The final popularity of the campaign can then be predicted. To demonstrate the efficacy of the MASEP-based approach, we apply it to predict the popularity of three types of online campaigns from different large-scale real-world datasets, namely, the total number of posts in retweeting cascades, the overall count of individual hashtags in posting streams, and the final number of sponsors for crowd-funding campaigns. In particular, using the initial 30% of each campaign data trace for training, our approach can achieve absolute prediction error (APE) of 13.25%, 15.7%, and 36.9% respectively for datasets of 3 different types of campaigns. This corresponds to a 26.1% to 63.2% reduction in prediction error when comparing to state-of-the-art approaches including SEISMIC, SpikeM, and STRM.
Anomaly Detection with Partially Observed Anomalies
Authors: Ya-Lin Zhang, Longfei Li, Jun Zhou, Xiaolong Li, Zhi-Hua Zhou
Venue: WWW
Year: 2018

In this paper, we consider the problem of anomaly detection. Previous studies mostly deal with this task in either supervised or unsupervised manner according to whether label information is available. However, there always exists settings which are different from the two standard manners. In this paper, we address the scenario when anomalies are partially observed, i.e., we are given a large amount of unlabeled instances as well as a handful labeled anomalies. We refer to this problem as anomaly detection with partially observed anomalies, and proposed a two-stage method ADOA to solve it. Firstly, by addressing the difference between the anomalies, the observed anomalies are clustered, while the unlabeled instances are filtered to get potential anomalies and reliable normal instances. Then, with the above instances, a weight is attached to each instance according to the confidence of its label, and a weighted multi-class model is built, which will be further used to distinguish different anomalies to the normal instances. Experimental results show that in the aforementioned setting, existing methods behave unsatisfactorily and the proposed method performs significantly better than all these methods, which validates the effectiveness of the proposed approach.
The Whole-Page Optimization via Dynamic Ad Allocation
Authors: Weiru Zhang, Chao Wei, Xiaonan Meng, Yi Hu, Hao Wang
Venue: WWW
Year: 2018

Modern search engines present result pages composed of two most prominent types of information: sponsored and organic search results. The whole-page results must satisfy user's information inquiry while sponsored ad alongside the search results has become a key monetization strategy for the platform. Against the backdrop of this situation, a basic question has received comparatively little attention: how many ads are good enough to get higher user satisfaction and better monetization Most search engines always display a fixed number of ads or use heuristic rules to determine the number of ads. In this paper, we formulate the task of finding the best number of ads into a linear programming optimization problem, for which we propose a novel online algorithm to solve. We have conducted several offline experiments and tested our approach in Alibaba E-commerce platform. The experimental results show that the platform could achieve higher revenue and more clicks simultaneously by the proposed algorithm.
User-guided Hierarchical Attention Network for Multi-modal Social Image Popularity Prediction
Authors: Wei Zhang, Wen Wang, Jun Wang, Hongyuan Zha
Venue: WWW
Year: 2018

Popularity prediction for the growing social images has opened unprecedented opportunities for wide commercial applications, such as precision advertising and recommender system. While a few studies have explored this significant task, little research has addressed its unstructured properties of both visual and textual modalities, and further considered to learn effective representation from multi-modalities for popularity prediction. To this end, we propose a model named User-guided Hierarchical Attention Network (UHAN) with two novel user-guided attention mechanisms to hierarchically attend both visual and textual modalities. It is capable of not only learning effective representation for each modality, but also fusing them to obtain an integrated multi-modal representation under the guidance of user embedding. As no benchmark dataset exists, we extend a publicly available social image dataset by adding the descriptions of images. The comprehensive experiments have demonstrated the rationality of our proposed UHAN and its better performance than several strong alternatives.
Deep Collective Classification in Heterogeneous Information Networks
Authors: Yizhou Zhang, Yun Xiong, Xiangnan Kong, Shanshan Li, Jinhong Mi, Yangyong Zhu
Venue: WWW
Year: 2018

Collective classification has attracted considerable attention in the last decade, where the labels within a group of instances are correlated and should be inferred collectively, instead of independently. Conventional approaches on collective classification mainly focus on exploiting simple relational features (such ascount andexists aggregators on neighboring nodes). However, many real-world applications involve complex dependencies among the instances, which are obscure/hidden in the networks. To capture these dependencies in collective classification, we need to go beyond simple relational features and extract deep dependencies between the instances. In this paper, we study the problem of deep collective classification inHeterogeneous Information Networks (HINs), which involves different types of autocorrelations, from simple to complex relations, among the instances. Different from conventional autocorrelations, which are given explicitly by the links in the network, complex autocorrelations are obscure/hidden in HINs, and should be inferred from existing links in a hierarchical order. This problem is highly challenging due to the multiple types of dependencies among the nodes and the complexity of the relational features. In this study, we proposed a deep convolutional collective classification method, called GraphInception to learn the deep relational features in HINs. The proposed method can automatically generate a hierarchy of relational features with different complexities. Extensive experiments on four real-world networks demonstrate that our approach can improve the collective classification performance by considering deep relational features in HINs.
Analyzing and Predicting Emoji Usages in Social Media
Authors: Peijun Zhao, Jia Jia, Yongsheng An, Jie Liang, Lexing Xie, Jiebo Luo
Venue: WWW
Year: 2018

Emojis can be regarded as a language for graphical expression of emotions, and have been widely used in social media. They can express more delicate feelings beyond textual information and improve the effectiveness of computer-mediated communication. Recent advances in machine learning make it possible to automatic compose text messages with emojis. However, the usages of emojis can be complicated and subtle so that analyzing and predicting emojis is a challenging problem. In this paper, we first construct a benchmark dataset of emojis with tweets and systematically investigate emoji usages in terms of tweet content, tweet structure and user demographics. Inspired by the investigation results, we further propose a multitask multimodality gated recurrent unit (mmGRU) model to predict the categories and positions of emojis. The model leverages not only multimodality information such as text, image and user demographics, but also the strong correlations between emoji categories and their positions. Our experimental results show that the proposed method can significantly improve the accuracy for predicting emojis for tweets (+9.0% in F1-value for category and +4.6% in F1-value for position). Based on the experimental results, we further conduct a series of case studies to unveil how emojis are used in social media.
Making the Most Cost-effective Decision in Online Paid Q&A Community: An Expert Recommender System with Motivation Modeling and Knowledge Pricing
Authors: Yunhao Zheng, Xi Zhang, Yuting Xiao
Venue: WWW
Year: 2018

Recommending proper experts to knowledge buyers is a significant problem in online paid Q&A community (OPQC). Existing approaches for online expert recommendation have been mainly focused on exploiting semantic similarities and social network influence, while personalizing recommendation according to individuals' motivations has not received much attention. In this paper, we propose a personalized expert recommender system, which integrates buyer's motivation for knowledge, social influence, and money in a unified framework. As an innovative application of cognitive computing, our recommender system is capable of providing users with the best matching experts so as to help them make the most cost-effective choice in OPQC. To this end, Paragraph Vector technique is implemented to construct domain knowledge base (KB) in a multilayer information retrieval (IR) framework. Then we perform knowledge pricing based on buyer's query and bid in the context of bilateral monopoly knowledge market. After that, a Markov Chain based method with user motivation learning is introduced to find the best matching experts. Finally, we evaluate the proposed approach using datasets collected from two OPQC. The experimental results show encouraging success as effectively offering reasonable personalization options. As an innovative approach to solve the expert matching problem in OPQC, this research provides flexibility in customizing the recommendation heuristics based on user motivation, and demonstrate its contribution to a higher rate of optimal knowledge seller-buyer matching.
Open Information Extraction with Global Structure Constraints
Authors: Qi Zhu, Xiang Ren, Jingbo Shang, Yu Zhang, Frank F. Xu, Jiawei Han
Venue: WWW
Year: 2018

Extracting entities and their relations from text is an important task for understanding massive text corpora. Open information extraction (IE) systems mine relation tuples (i.e., entity arguments and a predicate string to describe their relation) from sentences. However, current open IE systems ignore the fact that global statistics in a large corpus can be collectively leveraged to identify high-quality sentence-level extractions. In this paper, we propose a novel open IE system, called ReMine, which integrates local context signal and global structural signal in a unified framework with distant supervision. The new system can be efficiently applied to different domains as it uses facts from external knowledge bases as supervision; and can effectively score sentence-level tuple extractions based on corpus-level statistics. Specifically, we design a joint optimization problem to unify (1) segmenting entity/relation phrases in individual sentences based on local context; and (2) measuring the quality of sentence-level extractions with a translating-based objective. Experiments on real-world corpora from different domains demonstrate the effectiveness and robustness of ReMine when compared to other open IE systems.
Dual Graph Convolutional Networks for Graph-Based Semi-Supervised Classification
Authors: Chenyi Zhuang, Qiang Ma
Venue: WWW
Year: 2018

The problem of extracting meaningful data through graph analysis spans a range of different fields, such as the internet, social networks, biological networks, and many others. The importance of being able to effectively mine and learn from such data continues to grow as more and more structured data become available. In this paper, we present a simple and scalable semi-supervised learning method for graph-structured data in which only a very small portion of the training data are labeled. To sufficiently embed the graph knowledge, our method performs graph convolution from different views of the raw data. In particular, a dual graph convolutional neural network method is devised to jointly consider the two essential assumptions of semi-supervised learning: (1) local consistency and (2) global consistency. Accordingly, two convolutional neural networks are devised to embed the local-consistency-based and global-consistency-based knowledge, respectively. Given the different data transformations from the two networks, we then introduce an unsupervised temporal loss function for the ensemble. In experiments using both unsupervised and supervised loss functions, our method outperforms state-of-the-art techniques on different datasets.
Journal Track Chairs' Welcome & Organization
Authors: Claudia d&apos, Amato, Francesco Marcelloni, Rudi Studer
Venue: WWW
Year: 2018

It is our great pleasure to welcome you to the first edition of WWW 2018 Journal Track. The track is new track within WWW conference series and it is intended as a forum for presentations of significant Web-related research results that have been published recently in well-known and established journals, and have never been presented at any Web-related conference. The goal is to give visibility of these results to a conference audience as well as to promote discussions concerning such results. The call for papers of the journal track was open for two categories of papers: 1) self-nominations from authors promoting their own journal publication(s) and matching the prerequisites reported above; 2) invited papers, selected on the basis of interest, appropriateness and attractiveness for the WWW 2018 audience, from articles published since January 1st 2015 (even only in the electronic version) in the following journals: Journal of Network and Computer Applications, Journal of Web Semantics, Semantic Web Journal, IEEE Transactions on Fuzzy Systems, IEEE Transactions on Neural Networks and Learning Systems, Journal of Machine Learning Research, Data Mining and Knowledge Discovery, ACM Transactions on the Web, ACM Computing Surveys, Knowledge-based systems, Artificial Intelligence. Exceptions have been also considered for papers judged as potentially very influential but published before January 2015 or in a journal not included in the list. We received 61 submissions and accepted 12 of them for presentation to the WWW 2018 Journal Track. The papers have been evaluated according to the following criteria: novelty, relevance to the conference, quality of the extended abstract, metrics values (e.g. number of citations) computed for the original journal paper, representativeness and impact factor of the journals in which the papers have been published (secondarily). We also took in account the coverage of the different areas related to WWW.
Challenges Track Chairs' Welcome
Authors: Mathieu d&apos, Aquin, Elena Cabrio
Venue: WWW
Year: 2018

It is our great pleasure to welcome you to the WWW 2018 Challenges Track. It is the first time that the WWW conference includes such a track, which aim was to showcase the maturity of the state of the art on tasks common to the Web community and adjacent academic communities, in a controlled setting of rigorous evaluation. Through our call for challenge organisation, we also wanted to see which open questions might be seen as most relevant in this community today, and how groups of researchers might come together around shared resources (e.g. datasets) to address those questions in a hands-on, practical way. We received five proposals for challenges, and selected four of them, which were proposed by well established researchers in their respective domains. The topics addressed varied from being purely focused on a domain specific task, without constraints on the technical approach to take (e.g. music genre recognition) to fundamentally technical tasks, which can be seen as useful across domains (question answering), with two of the challenges representing a mix of both.
AFEL - Analytics for Everyday Learning
Authors: Mathieu d&apos, Aquin, Dominik Kowald, Angela Fessl, Elisabeth Lex, Stefan Thalmann
Venue: WWW
Year: 2018

The goal of AFEL is to develop, pilot and evaluate methods and applications, which advance informal/collective learning as it surfaces implicitly in online social environments. The project is following a multi-disciplinary, industry-driven approach to the analysis and understanding of learner data in order to personalize, accelerate and improve informal learning processes. Learning Analytics and Educational Data Mining traditionally relate to the analysis and exploration of data coming from learning environments, especially to understand learners' behaviours. However, studies have for a long time demonstrated that learning activities happen outside of formal educational platforms, also. This includes informal and collective learning usually associated, as a side effect, with other (social) environments and activities. Relying on real data from a commercially available platform, the aim of AFEL is to provide and validate the technological grounding and tools for exploiting learning analytics on such learning activities. This will be achieved in relation to cognitive models of learning and collaboration, which are necessary to the understanding of loosely defined learning processes in online social environments. Applying the skills available in the consortium to a concrete set of live, industrial online social environments, AFEL will tackle the main challenges of informal learning analytics through 1) developing the tools and techniques necessary to capture information about learning activities from (not necessarily educational) online social environments; 2) creating methods for the analysis of such informal learning data, based on combining feature engineering and visual analytics with cognitive models of learning and collaboration; and 3) demonstrating the potential of the approach in improving the understanding of informal learning, and the way it is better supported; 4) evaluate all the former items in real world large scale applications and platforms.
Protecting Social Network Users from Impersonation
Venue: WWW
Year: 2018

Malicious actors on social networks often create fake accounts that impersonate legitimate users in order to carry out scams, send spam and phishing messages, or engage in other abusive behavior. The act of assuming a real person»s identity gives the bad actor an additional level of trust that can make the attack more likely to succeed. In addition to the direct damage caused by the fraudulent activity, impersonation erodes users» trust in the platform and damages the brand of the legitimate user being impersonated. In this paper, we present a large-scale system that identifies and takes action on impersonation profiles on a social network. The system is composed of three tiers of protection: (1) an offline system that protects all users by searching for profile data duplicity combined with suspicious signals; (2) an offline system with stricter logic that proactively protects high-profile users in population segments that have historically been targeted for impersonation; and (3) an online system that provides low-latency protection to a manually curated list of individuals that have been actively targeted by impersonation. The tiers build on one another with decreasing detection latency and more targeted user coverage. We have deployed our system on the professional social network LinkedIn and drastically reduced the extent of impersonation abuse on LinkedIn. Specifically, we observed a reduction in the impersonator median time-to-detect of more than $98%$, a more than fourfold improvement in recall (as measured on a monthly basis) and reduction over $95%$ in the number of impersonation reports by the most targeted company on LinkedIn.
Representation Learning on Networks: Theories, Algorithms, and Applications
Authors: Jie Tang, Yuxiao Dong
Venue: WWW
Year: 2019

Network representation learning offers a revolutionary paradigm for mining and learning with network data. In this tutorial, we will give a systematic introduction for representation learning on networks. We will start the tutorial with industry examples from Alibaba, AMiner, Microsoft Academic, WeChat, and XueTangX to explain how network analysis and graph mining on the Web are benefiting from representation learning. Then we will comprehensively introduce both the history and recent advances on network representation learning, such as network embedding and graph neural networks. Uniquely, this tutorial aims to provide the audience with the underlying theories in network representation learning, as well as our experience in translating this line of research into real-world applications on the Web. Finally, we will release public datasets and benchmarks for open and reproducible network representation learning research. The tutorial accompanying page is at https://aminer.org/nrl_www2019.
Continuous Analytics of Web Streams
Authors: Riccardo Tommasini, Robin Keskisärkkä, Jean-Paul Calbimonte, Eva Blomqvist, Emanuele Della Valle, Albert Bifet
Venue: WWW
Year: 2019

This half-day tutorial provides a comprehensive introduction to web stream processing, including the fundamental stream reasoning concepts, as well as an introduction to practical implementations and how to use them in concrete web applications. To this extent, we intend to (1) survey existing research outcomes from Stream Reasoning / RDF Stream Processing that arise in querying, reasoning on and learning from a variety of highly dynamic data, (2) introduce deductive and inductive stream reasoning techniques as powerful tools to use when addressing a data-centric problem characterized both by variety and velocity, (3) present a relevant use-case, which requires to address data velocity and variety simultaneously on the web, and guide the participants in developing a web stream processing application.