OpenRaiser/Intern-Atlas huggingface dataset:
Recommendation in Context-Rich Environment: An Information Network Analysis Approach
Authors: Yizhou Sun, Hongzhi Yin, Xiang Ren
Venue: WWW
Year: 2017

Recommendation has received tremendous attention recently due to its wide and successful applications across different domains. Different from traditional setting of recommendation tasks, modern recommendation tasks are usually exposed in a context-rich environment. For example, in addition to a user-item rating matrix, users and items are connected to other objects via different relationships and they are usually associated with rich attributes, such as text and spatio-temporal information. It turns out that heterogeneous information network serves a natural data model to capture the rich context of these recommendation tasks. In this tutorial, we will systematically introduce the methodologies of using heterogeneous information network mining approach to solve recommendation tasks, and demonstrate the effectiveness of such methods using different applications, ranging from collaboration recommendation in scientific research network to job recommendation in professional social network, and to drug discovery in biomedical networks. The topics to be covered in the tutorial include: (1) overall introduction; (2) recommendation in heterogeneous information networks, which introduces the general methodology of how to model the recommendation problem as a heterogeneous information network mining problem; (3) recommendation in a text-rich setting, where the information network is further enriched by refined analysis of text information; (4) recommendation with spatio-temporal information, where entities and relationships in the network are associated with spatio-temporal attributes; and (5) research frontiers for context-rich recommendation.
Some Recipes Can Do More Than Spoil Your Appetite: Analyzing the Security and Privacy Risks of IFTTT Recipes
Authors: Milijana Surbatovich, Jassim Aljuraidan, Lujo Bauer, Anupam Das, Limin Jia
Venue: WWW
Year: 2017

The use of end-user programming, such as if-this-then-that (IFTTT), is becoming increasingly common. Services like IFTTT allow users to easily create new functionality by connecting arbitrary Internet-of-Things (IoT) devices and online services using simple if-then rules, commonly known as recipes. However, such convenience at times comes at the cost of security and privacy risks for end users. To gain an in-depth understanding of the potential security and privacy risks, we build an information-flow model to analyze how often IFTTT recipes involve potential integrity or secrecy violations. Our analysis finds that around 50% of the 19,323 unique recipes we examined are potentially unsafe, as they contain a secrecy violation, an integrity violation, or both. We next categorize the types of harm that these potentially unsafe recipes can cause to users. After manually examining a random selection of potentially unsafe recipes, we find that recipes can not only lead to harms such as personal embarrassment but can also be exploited by an attacker, e.g., to distribute malware or carry out denial-of-service attacks. The use of IoT devices and services like IFTTT is expected only to grow in the near future; our analysis suggests users need to be both informed about and protected from these emerging threats to which they could be unwittingly exposing themselves.
Learning Extreme Multi-label Tree-classifier via Nearest Neighbor Graph Partitioning
Authors: Yukihiro Tagami
Venue: WWW
Year: 2017

Web scale classification problems, such as Web page tagging and E-commerce product recommendation, are typically regarded as multi-label classification with an extremely large number of labels. In this paper, we propose GPT, which is a novel tree-based approach for extreme multi-label learning. GPT recursively splits a feature space with a hyperplane at each internal node, considering approximate k-nearest neighbor graph on the label space. We learn the linear binary classifiers using a simple optimization procedure. We conducted evaluations on several large-scale real-world data sets and compared our proposed method with recent state-of-the-art methods. Experimental results demonstrate the effectiveness of our proposed method.
AutoCyclone: Automatic Mining of Cyclic Online Activities with Robust Tensor Factorization
Authors: Tsubasa Takahashi, Bryan Hooi, Christos Faloutsos
Venue: WWW
Year: 2017

Given a collection of seasonal time-series, how can we find regular (cyclic) patterns and outliers (i.e. rare events)? These two types of patterns are hidden and mixed in the time-varying activities. How can we robustly separate regular patterns and outliers, without requiring any prior information?
Computational Models for Social Network Analysis: A Brief Survey
Authors: Jie Tang
Venue: WWW
Year: 2017

With the exponential growth of online social network services such as Facebook and Twitter, social networks and social medias become more and more important, directly influencing politics, economics, and our daily life. Mining big social networks aims to collect and analyze web-scale social data to reveal patterns of individual and group behaviors. It is an inherently interdisciplinary academic field which emerged from sociology, psychology, statistics, and graph theory. In this article, I briefly survey recent progress on social network mining with an emphasis on understanding the interactions among users in the large dynamic social networks. I will start with some basic knowledge for social network analysis, including methodologies and tools for macro-level, meso-level and microlevel social network analysis. Then I will give an overall roadmap of social network mining. After that, I will describe methodologies for modeling user behavior including state-of-the-art methods for learning user profiles, and introduce recent progress on modeling dynamics of user behaviors using deep learning. Then I will present models and algorithms for quantitative analysis on social interac- tions including homophily and social influence.Finally, I will introduce network structure model including social group formation, and network topology generation. We will introduce recent developed network embedding algorithms for modeling social networks with the embedding techniques. Finally, I will use several concrete examples from Alibaba, the largest online shopping website in the world, and WeChat, the largest social messaging service in China, to explain how online social networks influence our offline world.
From Citation Network to Study Map: A Novel Model to Reorganize Academic Literatures
Authors: Shibo Tao, Xiaorong Wang, Weijing Huang, Wei Chen, Tengjiao Wang, Kai Lei
Venue: WWW
Year: 2017

As the number of academic papers and new technologies soars, it has been increasingly difficult for researchers, especially beginners, to enter a new research field. Researchers often need to study a promising paper in depth to keep up with the forefront of technology. Traditional Query-Oriented study method is time-consuming and even tedious. For a given paper, existent academic search engines like Google Scholar tend to recommend relevant papers, failing to reveal the knowledge structure. The state-of-the-art Map-Oriented study methods such as AMiner and AceMap can structure scholar information, but they're too coarse-grained to dig into the underlying principles of a specific paper. To address this problem, we propose a Study-Map Oriented method and a novel model called RIDP (Reference Injection based Double-Damping PageRank) to help researchers study a given paper more efficiently and thoroughly. RIDP integrates newly designed Reference Injection based Topic Analysis method and Double-Damping PageRank algorithm to mine a Study Map out of massive academic papers in order to guide researchers to dig into the underlying principles of a specific paper. Experiment results on real datasets and pilot user studies indicate that our method can help researchers acquire knowledge more efficiently, and grasp knowledge structure systematically.
Structural Aspects of User Roles in Information Cascades
Authors: Io Taxidou, Peter M. Fischer
Venue: WWW
Year: 2017

Social media plays an important role for the exchange and dissemination of information among its users. In turn, online users shape social media by their interactions, status and online behaviour in general. These aspects differ massively from user to user, which has an impact on the outcome of information diffusion. Users on social media have been categorised to user roles according to their online behaviour. While there has been a lot of research on user roles and information diffusion in isolation, their combination has not been researched much. In this paper, we study their correlation in particular whether particular user roles occur in specific structural positions in information cascades. By testing several hypotheses we could confirm that there is indeed a correlation of these two aspects. However, some user roles demonstrate diverse behaviour with regard to their activity patterns and need further investigation.
Linked Data Indexing of Distributed Ledgers
Authors: Allan Third, John Domingue
Venue: WWW
Year: 2017

Searching for information in distributed ledgers is currently not an easy task, as information relating to an entity may be scattered throughout the ledger with no index. As distributed ledger technologies become more established, they will increasingly be used to represent real world transactions involving many parties and the search requirements will grow. An index providing the ability to search using domain specific terms across multiple ledgers will greatly enhance to power, usability and scope of these systems. We have implemented a semantic index to the Ethereum blockchain platform, to expose distributed ledger data as Linked Data. As well as indexing block- and transaction-level data according to the BLONDiE ontology, we have mapped smart contracts to the Minimal Service Model ontology, to take the first steps towards connecting smart contracts with Semantic Web Services.
Flipping MIS Classroom by Peers: Gateway to Student's Engagement Intention
Authors: Mathupayas Thongmak
Venue: WWW
Year: 2017

Flipped classroom is one approach of active learning leading to better students' learning, abilities, and academic performance. The aim of this study is to evaluate the effect of applying the flipped classroom approach to MIS course, to examine the antecedents of engagement intention after participating the flipped classroom by peers, and to guide key factors of success student presentations. The quasi-experimental retrospective pre-post design is adopted during the whole semester. Eight rounds of student presentations were conducted. Data were collected after each round. Descriptive statistics, t-test, simple linear regression, and multiple linear regression were used. The findings confirm the relationships among perceived usefulness, satisfaction, and engagement intention. There were significant differences among students' perceived usefulness and intention before and after applying the flipped activities. Audience students also suggest the proper settings for the flipped classroom by peers.
The Role of Crowdsourcing in the Emerging Internet-Of-Things
Authors: Ramine Tinati, Aastha Madaan, Wendy Hall
Venue: WWW
Year: 2017

In this position paper we wish to propose and discuss several open research questions associated with the IoT. In particular, we wish to consider how crowdsourcing can be used as a scalable, reliable, and sustainable approach to support various computationally difficult and ambiguous tasks recognised in IoT research. We illustrate our work by examining a number of use cases related to healthcare and smart cities, and finally consider the future development of the IoT eco-system with respect to the socio-technical philosophy and implementation of the Web Observatory.
Euclidean Image Embedding in view of Similarity Ranking in Auction Search by Image
Authors: Riku Togashi, Hideyuki Maeda, Vibhor Kanojia, Kousuke Morimoto, Sumio Fujita
Venue: WWW
Year: 2017

We propose an Euclidean embedding image representation, which serves to rank auction item images through wide range of semantic similarity spectrum, in the order of the relevance to the given query image much more effective than the baseline method in terms of a graded relevance measure. Our method uses three stream deep convolutional siamese networks to learn a distance metric and we leverage search query logs of an auction item search of the largest auction service in Japan. Unlike previous approaches, we define the inter-image relevance on the basis of user queries in the logs used to search each auction item, which enables us to acquire the image representation preserving the features concerning user intents in real e-commerce world.
Understanding Indoor Behavior: Where, What, with Whom?
Authors: Martin Tomko
Venue: WWW
Year: 2017

Capturing and understanding users' context is key to the success of applications such as computational advertising and recommender systems. Currently, context is usually inferred from a user's interaction history with Web content. As an ever-increasing portion of Web interaction occurs in a mobile context, the physical and social environment in which the interaction occurs is a key factor of user context and holds additional signals of user intent. In this talk, I will discuss recent work on understanding user behavior based on recorded traces of the cyber, social and physical component of users interaction with the in indoor environment (i.e., shopping malls), based on an extensive dataset of Wi-Fi logs. We will draw lessons for the future of indoor recommender services and context-aware mobile applications.
Serious Games for Dementia
Authors: Tiffany Tong, Jonathan H. Chan, Mark H. Chignell
Venue: WWW
Year: 2017

With the current phenomenon of aging populations in most parts of the world, there are corresponding increases in age-related conditions associated with impaired cognitive status, such as dementia and delirium. Cognitive status is a key component in carrying out activities of daily living such as walking and bathing, and departures from normal cognitive status may be indicators of acute (e.g., delirium) or chronic (e.g., dementia) conditions. Individuals with cognitive impairments may benefit from playing serious games, which are games designed for a primary purpose other than entertainment. Serious games can potentially assess a variety of factors associated with cognitive decline in dementia, while keeping individuals active and stimulated, thereby potentially slowing down or furthering cognitive decline. In this workshop paper, we discuss the development and use of serious games focusing on cognitive functions that are affected by the progression of dementia.
Scalable Motif-aware Graph Clustering
Authors: Charalampos E. Tsourakakis, Jakub Pachocki, Michael Mitzenmacher
Venue: WWW
Year: 2017

We develop new methods based on graph motifs for graph clustering, allowing more efficient detection of communities within networks. We focus on triangles within graphs, but our techniques extend to other clique motifs as well. Our intuition, which has been suggested but not formalized similarly in previous works, is that triangles are a better signature of community than edges. We therefore generalize the notion of conductance for a graph to triangle conductance, where the edges are weighted according to the number of triangles containing the edge. This methodology allows us to develop variations of several existing clustering techniques, including spectral clustering, that minimize triangles split by the cluster instead of edges cut by the cluster. We provide theoretical results in a planted partition model to demonstrate the potential for triangle conductance in clustering problems. We then show experimentally the effectiveness of our methods to multiple applications in machine learning and graph mining.
Exploring HTTP Header Manipulation In-The-Wild
Authors: Gareth Tyson, Shan Huang, Félix Cuadrado, Ignacio Castro, Vasile Claudiu Perta, Arjuna Sathiaseelan, Steve Uhlig
Venue: WWW
Year: 2017

Headers are a critical part of HTTP, and it has been shown that they are increasingly subject to middlebox manipulation. Although this is well known, little is understood about the general regional and network trends that underpin these manipulations. In this paper, we collect data on thousands of networks to understand how they intercept HTTP headers in-the-wild. Our analysis reveals that 25% of measured ASes modify HTTP headers. Beyond this, we witness distinct trends among different regions and AS types; e.g., we observe high numbers of cache headers in poorly connected regions. Finally, we perform an in-depth analysis of the types of manipulations and how they differ across regions.
Vol4All: A Volunteering Platform to Drive Innovation and Citizens Empowerment
Authors: Athena Vakali, Ioannis Dematis, Athanasios Tolikas
Venue: WWW
Year: 2017

Cities nowadays have embraced the digital era and continuously strive to merge technological advancements with the benefit of their social capital and communities. A major quest is to place humans and their competences at the center of the efforts towards sustainable and smart cities. Citizen societies have widely accepted and practiced volunteering for years now and already a great number of volunteering actions and networks have flourished, in support and aid to several communities in need. Most popular volunteering networks have greatly capitalized on the rapid advance and spread of Internet and Web technologies, which are ideal for coordinating and monitoring of the volunteering tasks. The Vol4All platform advances this trend, by building on extended Internet technologies in its aim to support citizens' activism towards novel urban social innovation. Vol4All enables ideas exchange and crowdsourcing by facilitating citizens' involvement in the realization of community projects. Volunteering actors (initiators, participants, stakeholders) can easily interact via the Vol4All platform which enables volunteering opportunities dynamic sharing, evolution and monitoring. Such opportunities can be initiated by any authorized stakeholders, with a publicly open interface which allows citizens commitment assessment, best practices highlights, and a gamification style of interaction such that volunteering becomes a societal and growth asset.
Graph of Words: Boosting Text Mining Tasks with Graphs
Authors: Michalis Vazirgiannis
Venue: WWW
Year: 2017

The Bag-of-words model has been the dominant approach for IR and Text mining for many years assuming the word independence and the frequencies as the main feature for feature selection and for query to document similarity. Although the long and successful usage, bag- of-words ignores words' order and distance within the document -- weakening thus the expressive power of the distance metrics. We propose graph-of-word, an alternative approach that capitalizes on a graph representation of documents and challenges the word independence assumption by taking into account words' order and distance. We applied graph-of-word in various tasks such as ad-hoc Information Retrieval, Single-Document Keyword Extraction, Text Categorization and Sub-event Detection in Textual Streams. In all cases the the graph of word approach, assisted by degeneracy at times, outperforms the state of the art base lines in all cases.
Correlation Clustering with Low-Rank Matrices
Authors: Nate Veldt, Anthony Ian Wirth, David F. Gleich
Venue: WWW
Year: 2017

Correlation clustering is a technique for aggregating data based on qualitative information about which pairs of objects are labeled `similar' or `dissimilar.' Because the optimization problem is NP-hard, much of the previous literature focuses on finding approximation algorithms. In this paper we explore how to solve the correlation clustering objective exactly when the data to be clustered can be represented by a low-rank matrix. We prove in particular that correlation clustering can be solved in polynomial time when the underlying matrix is positive semidefinite with small constant rank, but that the task remains NP-hard in the presence of even one negative eigenvalue. Based on our theoretical results, we develop an algorithm for efficiently ``solving'' low-rank positive semidefinite correlation clustering by employing a procedure for zonotope vertex enumeration. We demonstrate the effectiveness and speed of our algorithm by using it to solve several clustering problems on both synthetic and real-world data.
Sampling from Social Networks with Attributes
Authors: Claudia Wagner, Philipp Singer, Fariba Karimi, Jürgen Pfeffer, Markus Strohmaier
Venue: WWW
Year: 2017

Sampling from large networks represents a fundamental challenge for social network research. In this paper, we explore the sensitivity of different sampling techniques (node sampling, edge sampling, random walk sampling, and snowball sampling) on social networks with attributes. We consider the special case of networks (i) where we have one attribute with two values (e.g., male and female in the case of gender), (ii) where the size of the two groups is unequal (e.g., a male majority and a female minority), and (iii) where nodes with the same or different attribute value attract or repel each other (i.e., homophilic or heterophilic behavior). We evaluate the different sampling techniques with respect to conserving the position of nodes and the visibility of groups in such networks. Experiments are conducted both on synthetic and empirical social networks. Our results provide evidence that different network sampling techniques are highly sensitive with regard to capturing the expected centrality of nodes, and that their accuracy depends on relative group size differences and on the level of homophily that can be observed in the network. We conclude that uninformed sampling from social networks with attributes thus can significantly impair the ability of researchers to draw valid conclusions about the centrality of nodes and the visibility or invisibility of groups in social networks.
How Users Explore Ontologies on the Web: A Study of NCBO's BioPortal Usage Logs
Authors: Simon Walk, Lisette Espin Noboa, Denis Helic, Markus Strohmaier, Mark A. Musen
Venue: WWW
Year: 2017

Ontologies in the biomedical domain are numerous, highly specialized and very expensive to develop. Thus, a crucial prerequisite for ontology adoption and reuse is effective support for exploring and finding existing ontologies. Towards that goal, the National Center for Biomedical Ontology (NCBO) has developed BioPortal---an online repository containing more than 500 biomedical ontologies. In 2016, BioPortal represents one of the largest portals for exploration of semantic biomedical vocabularies and terminologies, which is used by many researchers and practitioners. While usage of this portal is high, we know very little about how exactly users search and explore ontologies and what kind of usage patterns or user groups exist in the first place. Deeper insights into user behavior on such portals can provide valuable information to devise strategies for a better support of users in exploring and finding existing ontologies, and thereby enable better ontology reuse. To that end, we study and group users according to their browsing behavior on BioPortal and use data mining techniques to characterize and compare exploration strategies across ontologies. In particular, we were able to identify seven distinct browsing types, all relying on different functionality provided by BioPortal. For example, Search Explorers extensively use the search functionality while Ontology Tree Explorers mainly rely on the class hierarchy for exploring ontologies. Further, we show that specific characteristics of ontologies influence the way users explore and interact with the website. Our results may guide the development of more user-oriented systems for ontology exploration on the Web.