OpenRaiser/Intern-Atlas huggingface dataset:
Platform Criminalism: The 'Last-Mile' Geography of the Darknet Market Supply Chain
Authors: Martin Dittus, Joss Wright, Mark Graham
Venue: WWW
Year: 2018

Does recent growth of darknet markets signify a slow reorganisation of the illicit drug trade? Where are darknet markets situated in the global drug supply chain? In principle, these platforms allow producers to sell directly to end users, bypassing traditional trafficking routes. And yet, there is evidence that many offerings originate from a small number of highly active consumer countries, rather than from countries that are primarily known for drug production. In a large-scale empirical study, we determine the darknet trading geography of three plant-based drugs across four of the largest darknet markets, and compare it to the global footprint of production and consumption for these drugs. We present strong evidence that cannabis and cocaine vendors are primarily located in a small number of consumer countries, rather than producer countries, suggesting that darknet trading happens at the »last mile», possibly leaving old trafficking routes intact. A model to explain trading volumes of opiates is inconclusive. We cannot find evidence for significant production-side offerings across any of the drug types or marketplaces. Our evidence further suggests that the geography of darknet market trades is primarily driven by existing consumer demand, rather than new demand fostered by individual markets.
GPSP: Graph Partition and Space Projection based Approach for Heterogeneous Network Embedding
Authors: Wenyu Du, Shuai Yu, Min Yang, Qiang Qu, Jia Zhu
Venue: WWW
Year: 2018

In this paper, we propose GPSP, a novel Graph Partition and Space Projection based approach, to learn the representation of a heterogeneous network that consists of multiple types of nodes and links. Concretely, we first partition the heterogeneous network into homogeneous and bipartite subnetworks. Then, the projective relations hidden in bipartite subnetworks are extracted by learning the projective embedding vectors. Finally, we concatenate the projective vectors from bipartite subnetworks with the ones learned from homogeneous subnetworks to form the final representation of the heterogeneous network. Extensive experiments are conducted on a real-life dataset. The results demonstrate that GPSP outperforms the state-of-the-art baselines in two key network mining tasks: node classification and clustering.
MemeSequencer: Sparse Matching for Embedding Image Macros
Authors: Abhimanyu Dubey, Esteban Moro, Manuel Cebrián, Iyad Rahwan
Venue: WWW
Year: 2018

The analysis of the creation, mutation, and propagation of social media content on the Internet is an essential problem in computational social science, affecting areas ranging from marketing to political mobilization. A first step towards understanding the evolution of images online is the analysis of rapidly modifying and propagating memetic imagery or "memes". However, a pitfall in proceeding with such an investigation is the current incapability to produce a robust semantic space for such imagery, capable of understanding differences in Image Macros. In this study, we provide a first step in the systematic study of image evolution on the Internet, by proposing an algorithm based on sparse representations and deep learning to decouple various types of content in such images and produce a rich semantic embedding. We demonstrate the benefits of our approach on a variety of tasks pertaining to memes and Image Macros, such as image clustering, image retrieval, topic prediction and virality prediction, surpassing the existing methods on each. In addition to its utility on quantitative tasks, our method opens up the possibility of obtaining the first large-scale understanding of the evolution and propagation of memetic imagery.
Incorporating Statistical Features in Convolutional Neural Networks for Question Answering with Financial Data
Authors: Shijia E, Shiyao Xu, Yang Xiang
Venue: WWW
Year: 2018

The goal of question answering with financial data is selecting sentences as answers from the given documents for a question. The core of the task is computing the similarity score between the question and answer pairs. In this paper, we incorporate statistical features such as the term frequency-inverse document frequency (TF-IDF) and the word overlap in convolutional neural networks to learn optimal vector representations of question-answering pairs. The proposed model does not depend on any external resources and can be easily extended to other domains. Our experiments show that the TF-IDF and the word overlap features can improve the performance of basic neural network models. Also, with our experimental results, we can prove that models based on the margin loss training achieve better performance than the traditional classification models. When the number of candidate answers for each question is 500, our proposed model can achieve 0.622 in Top-1 accuracy (Top-1), 0.654 in mean average precision (MAP), 0.767 in normalized discounted cumulative gain (NDCG), and 0.701 in bilingual evaluation understudy (BLEU). If the number of candidate answers is 30, all the values of the evaluation metrics can reach more than 90%.
Towards Recommendation Using Interest-Based Communities in Attributed Social Networks
Authors: Amani H. B. Eissa, Mohamed E. El-Sharkawi, Hoda M. O. Mokhtar
Venue: WWW
Year: 2018

Social networks can be modeled as attributed networks whose nodes represent users, edges represent relationships among users (e.g. friendship/follow) and attribute vectors hold properties of nodes and/or edges. In this paper, we consider friends' recommendation based on interest-based communities generated from topic based attributed social networks (TbASN). In our model, an attribute vector is not just a container for explicit users' profile data that is stored in social network's dataset, but rather holds topic vectors that are derived from analyzing the implicit interest of users' that are aggregated from his/her posts on the social network (e.g. tweets in Twitter, posts in Facebook). In our framework, topics of interest are represented as a hierarchy of topics (Topics/Subtopics) forming hierarchical interest-based communities. Users within each interest-based community are clustered according to their profile features (age, location, education etc.). Those clusters are later used in recommendations where recommendations target members of the same cluster to guarantee the quality and coherence of recommendations. In addition, we propose a recommendation selection approach to handle the large number of recommended candidates. The main advantage of the proposed approach is that it considers multiple criteria for candidate selection including the number of common communities, the resemblance in basic features, as well as network proximity. In addition to recommending friends of similar interests, frequent pattern mining is used to discover frequently occurring interests in order to be used in recommending communities for users to join. Although our approach is generic and can be applied to most of the existing social networks, we used Twitter as our target social network.
Cultural Heritage Resources Profiling: Ontology-based Approach
Authors: Mohamed Ben Ellefi, Odile Papini, Djamal Merad, Jean-Marc Boï, Jean-Philip Royer, Jérôme Pasquet, Jean-Christophe Sourisseau, Filipe Castro, Mohamad Motasem Nawaf, Pierre Drap
Venue: WWW
Year: 2018

Cultural heritage (CH) resources are very heterogeneous since the information was collected from vast diversity of cultural sites and digitally recorded in different formats. With the progress of 3D technologies, photogrammetry techniques become the adopted solution for representing CH artifacts by turning photos from small finds, to entire landscapes, into accurate 3D models. To meet knowledge representation with cultural heritage photogrammetry, this paper proposes an ontology-profiling method for modeling a real case of archaeological amphorae. The ontological profile consists of all needed information to represent a CH resource including typology attributes, geo-spatial information and photogrammetry process. An example illustrating the applicability of this profiling method to the problem of CH resources conceptualization is presented. We also outline our perspectives for using ontologies in data-driven science, in particular on modeling a complete pipeline that manages both the photogrammetric process and the archaeological knowledge.
Graph Aggregation
Authors: Ulle Endriss, Umberto Grandi
Venue: WWW
Year: 2018

Graph aggregation is the process of computing a single output graph that constitutes a good compromise between several input graphs, each provided by a different source. One needs to perform graph aggregation in a wide variety of situations, e.g., when applying a voting rule (graphs as preference orders), when consolidating conflicting views regarding the relationships between arguments in a debate (graphs as abstract argumentation frameworks), or when computing a consensus between several alternative clusterings of a given dataset (graphs as equivalence relations). Other potential applications include belief merging, data integration, and social network analysis. In this short paper, we review a recently introduced formal framework for graph aggregation that is grounded in social choice theory. Our focus is on understanding which properties shared by the individual input graphs will transfer to the output graph returned by a given aggregation rule. Our main result is a powerful impossibility theorem that generalises Arrow's seminal result regarding the aggregation of preference orders to a large collection of different types of graphs. We also provide a discussion of existing and potential applications of graph aggregation.
How to Support Human Operator in "Uncertainty" Managing during the Ontology Learning Process
Authors: Roberto Enea, Maria Teresa Pazienza, Andrea Turbati, Alessandro Colantonio
Venue: WWW
Year: 2018

Creating ontologies is an essential while challenging task to be performed by either a human or a system: on one hand it is excessively burdensome for a human operator, on the other it is very complex also for a machine due to the not negligible amount of "uncertainty" that it must be able to manage. In the last years, some attempts have been made to automate this process, but at present, due to the large number of aspects to be covered in the automatic creation of an ontology (such as Domain terminology extraction, Concept discovery, Concept hierarchy derivation, ") satisfactory solutions have not been reached yet. In order to produce efficient tools for both creation and enrichment of ontologies, the participation of the human in such a process still seems necessary. Our approach, that foresees a broader framework for ontology learning, is based by first on the automatic extraction of triples from heterogeneous sources, then on the presentation of the most reliable triples to the human operator for validation purposes. The system provides the user with a series of graphical representations that can give him an overview of the level of uncertainty of the automatically generated ontology. Then provides the user with the possibility to perform SPARQL what-if queries, (i.e. assuming as true the triples filtered according to the level of confidence, the source and the structure of the triples).
Community detection in Attributed Network
Authors: Issam Falih, Nistor Grozavu, Rushed Kanawati, Younès Bennani
Venue: WWW
Year: 2018

Graph clustering techniques are very useful for detecting densely connected groups in large graphs. Many existing graph clustering methods mainly focus on the topological structure, but ignore the vertex properties. Existing graph clustering methods have been recently extended to deal with nodes attribute. First we motivate the interest in the study of this issue. Then we review the main approaches proposed to deal with this problem. We propose a comparative study of some existing attributed network community detection algorithm on both synthetic data and on real world data.
COEVOLVE: A Joint Point Process Model for Information Diffusion and Network Co-evolution
Authors: Mehrdad Farajtabar, Manuel Gomez-Rodriguez, Yichen Wang, Shuang Li, Hongyuan Zha, Le Song
Venue: WWW
Year: 2018

Information diffusion in online social networks is affected by the underlying network topology, but it also has the power to change it. Online users are constantly creating new links when they are exposed to new information sources, and in turn these links are alternating the way information spreads. However, these two highly intertwined stochastic processes---information diffusion and network evolution---have been typically studied separately, ignoring their co-evolutionary dynamics. In this work, we propose a temporal point process model, COEVOLVE, for such joint dynamics, allowing the intensity of one process to be modulated by that of the other. The model allows us to efficiently simulate interleaved diffusion and network events, and generate traces obeying common diffusion and network patterns observed in real-world networks. Moreover, we develop a convex optimization framework to learn the parameters of the model from historical diffusion and network evolution traces. Experiments in both synthetic data and real data gathered from Twitter show that our model provides a good fit to the data as well as more accurate predictions than alternatives.
Question Answering Mediated by Visual Clues and Knowledge Graphs
Authors: Fabrício Firmino de Faria, Ricardo Usbeck, Alessio Sarullo, Tingting Mu, André Freitas
Venue: WWW
Year: 2018

This challenge focuses on the use of semantic representation methods to support Visual Question Answering: given a large image collection, find a set of images matching natural language queries. The task supports advancing the state-of-the-art in Visual Question Answering by focusing on methods which explore the interplay between contemporary machine learning techniques, semantic representation and reasoning mechanisms.
Learning to Collaborate: Multi-Scenario Ranking via Multi-Agent Reinforcement Learning
Authors: Jun Feng, Heng Li, Minlie Huang, Shichen Liu, Wenwu Ou, Zhirong Wang, Xiaoyan Zhu
Venue: WWW
Year: 2018

Ranking is a fundamental and widely studied problem in scenarios such as search, advertising, and recommendation. However, joint optimization for multi-scenario ranking, which aims to improve the overall performance of several ranking strategies in different scenarios, is rather untouched. Separately optimizing each individual strategy has two limitations. The first one is lack of collaboration between scenarios meaning that each strategy maximizes its own objective but ignores the goals of other strategies, leading to a sub-optimal overall performance. The second limitation is the inability of modeling the correlation between scenarios meaning that independent optimization in one scenario only uses its own user data but ignores the context in other scenarios. In this paper, we formulate multi-scenario ranking as a fully cooperative, partially observable, multi-agent sequential decision problem. We propose a novel model named Multi-Agent Recurrent Deterministic Policy Gradient (MA-RDPG) which has a communication component for passing messages, several private actors (agents) for making actions for ranking, and a centralized critic for evaluating the overall performance of the co-working actors. Each scenario is treated as an agent (actor). Agents collaborate with each other by sharing a global action-value function (the critic) and passing messages that encodes historical information across scenarios. The model is evaluated with online settings on a large E-commerce platform. Results show that the proposed model exhibits significant improvements against baselines in terms of the overall performance.
Querying Wikimedia Images using Wikidata Facts
Authors: Sebastián Ferrada, Nicolás Bravo, Benjamin Bustos, Aidan Hogan
Venue: WWW
Year: 2018

Despite its importance to the Web, multimedia content is often neglected when building and designing knowledge-bases: though descriptive metadata and links are often provided for images, video, etc., the multimedia content itself is often treated as opaque and is rarely analysed. IMGpedia is an effort to bring together the images of Wikimedia Commons (including visual information), and relevant knowledge-bases such as Wikidata and DBpedia. The result is a knowledge-base that incorporates similarity relations between the images based on visual descriptors, as well as links to the resources of Wikidata and DBpedia that relate to the image. Using the IMGpedia SPARQL endpoint, it is then possible to perform visuo-semantic queries, combining the semantic facts extracted from the external resources and the similarity relations of the images. This paper presents a new web interface to browse and explore the dataset of IMGpedia in a more friendly manner, as well as new visuo-semantic queries that can be answered using 6 million recently added links from IMGpedia to Wikidata. We also discuss future directions we foresee for the IMGpedia project.
Towards Offline Support for RESTful Systems
Authors: Tobias Fertig, Peter Braun
Venue: WWW
Year: 2018

Representational State Transfer (REST) is an efficient and by now established architectural style for distributed hypermedia systems. However, REST has not been designed for offline operations, yet many applications must also keep functioning when going offline for more than a few seconds. Burdening the programmer with knowledge about offline status is undesirable. RESTful applications can be described by a formal model. Therefore, we define a function to derive a formal model of the proxy for handling offline support on the client-side. We then extend existing caching approaches so that a client-side proxy can transparently hide the offline status from the application. We validate our solution with a proxy layer that covers all test cases derived from the model. Using our model and proxy, clients do not have to know and worry about whether they are online or offline.
Building Knowledge Maps of Web Graphs
Authors: Valeria Fionda, Giuseppe Pirrò, Claudio Gutierrez
Venue: WWW
Year: 2018

We research the problem of building knowledge maps of graph-like information. We live in the digital era and similarly to the Earth, the Web is simply too large and its interrelations too complex for anyone to grasp much of it through direct observation. Thus, the problem of applying cartographic principles also to digital landscapes is intriguing. We introduce a mathematical formalism that captures the general notion of map of a graph and enables its development and manipulation in a semi-automated way. We describe an implementation of our formalism on the Web of Linked Data graph and discuss algorithms that efficiently generate and combine (via an algebra) regions and maps. Finally, we discuss examples of knowledge maps built with a tool implementing our framework.
Comparison of Users' and Designers' Differences in Mobile Shopping App Interface Preferences and Designs
Authors: Yu Fu, Dongliang Zhang, Hao Jiang
Venue: WWW
Year: 2018

Besides usability, visual appearance also plays an important role in influencing users' attitudes towards the mobile shopping apps. This article presents a pilot study that explores users' and designers' preferences for mobile shopping app user interfaces (UIs). The study consisted of two phases. (1) Eliciting participants' perception of UIs similarity by sorting, using DISTATIS and cluster analysis, the UIs similarity perceptual space was identified. (2) Eliciting participants' overall preference by rating. The results identified three typical UIs and the distribution of ideal preference UIs for users and designers. Last, users and designers' differences in the UI preference were discussed.
The BIG Web Track Chairs' Welcome & Organization
Authors: Evgeniy Gabrilovich, Kira Radinsky, Kuansan Wang
Venue: WWW
Year: 2018

It is our great pleasure to welcome you to the BIG Web Track of the Web Conference 2018. Many of today's most successful enterprises in business and in science are built on the collection and analysis of data. The sheer volume and richness of these data sets has stimulated a massive wave of innovation. In addition, this revolution has also sparked important debate on data privacy policies, ethics, and governance. This track started as a co-located event called BigData Innovators Gathering (BIG) with a vision to bring together academic and industry leaders in the Big Data space to share the state of the art and its successful applications in business. This event will be co-located with the Web conference for the fifth time, but now as a fully fledged alternate track named The BIG Web in The Web Conference 2018 in Lyon. This year's track consists of two keynotes, a panel on machine learning in the field of medicine, and 11 invited talks. In addition, we have accepted 6 papers from 35 submissions (with an acceptance ratio of 17%).
The Liquid User Experience API
Authors: Andrea Gallidabino, Cesare Pautasso
Venue: WWW
Year: 2018

In the past years the average number of Web-enabled devices owned by each user has significantly increased. Liquid Web applications enable users to take advantage of all their devices sequentially to migrate their running applications across them or simultaneously when running different views of the same application at the same time on each device. Developers of liquid Web application need to control how to expose the liquid behavior of their cross-device Web applications to the users. To do so, they can use the API of Liquid.js we describe in this paper. Liquid.js is a framework for building component-based rich Web applications which run across multiple Web-enabled devices. The framework is based on technologies such as Polymer, WebRTC, WebWorkers, PouchDB and Yjs. Liquid.js helps to build decentralized Web applications whose components can seamlessly flow directly between Web browsers carrying along their execution state. The Liquid.js API gives developers fine-grained control over the liquid user experience primitives, device discovery, and the lifecycle of liquid Web components.
Contextual Word Embedding: A Case Study in Clustering Tweets about Emergency Situations
Authors: Debasis Ganguly, Kripabandhu Ghosh
Venue: WWW
Year: 2018

Effective clustering of short documents, such as tweets, is difficult because of the lack of sufficient semantic context. Word embedding is a technique that is effective in addressing this lack of semantic context. However, the process of word vector embedding, in turn, relies on the availability of sufficient contexts to learn the word associations. To get around this problem, we propose a novel word vector training approach that leverages topically similar tweets to better learn the word associations. We test our proposed word embedding approach by clustering a collection of tweets on disasters. We observe that the proposed method improves clustering effectiveness by up to 14%.