Fusion of text and graph information for machine learning problems on networks
Today, increased attention is drawn towards network representation learning, a technique that maps nodes of a network into vectors of a low-dimensional embedding space. A network embedding constructed this way aims to preserve nodes similarity and other specific network properties. Embedding vectors can later be used for downstream machine learning problems, such as node classification, link prediction and network visualization. Naturally, some networks have text information associated with them. For instance, in a citation network, each node is a scientific paper associated with its abstract or title; in a social network, all users may be viewed as nodes of a network and posts of each user as textual attributes. In this work, we explore how combining existing methods of text and network embeddings can increase accuracy for downstream tasks and propose modifications to popular architectures to better capture textual information in network embedding and fusion frameworks.
Trading processes is a vital part of human life and any unstable situation results in the change of living conditions of individuals. We study the power of each country in terms of produce trade. Trade relations between countries are represented as a network, where vertices are territories and edges are export flows. As flows of products between participants are heterogeneous we consider various groups of substitute goods (cereals, fish, vegetables). We detect key participants affecting food retail with the use of classical centrality measures. We also perform clustering procedure in order to find communities in networks.
Welcome to the 2018 ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL) in Fort Worth, Texas!
It is our great pleasure to present the proceedings of the 18th JCDL. This year's conference theme -- From Data to Wisdom: Resilient Integration across Societies, Disciplines, and Systems -- reflects the progress of digital libraries into a mature research field. JCDL has always invited a broad range of reporting on research, development, and best practices, ranging across theories, systems, services, and applications in the field. This year's call focused on inviting contributions from many different disciplines (also newcomers and associated disciplines) and different stakeholders (researchers and practitioners), with the intent to showcase the diverse methods and research mix in the DL community.
We believe that this goal has been achieved. This year's sessions cover topics about different object types (e.g., text, multimedia), different domains (science, archives), different digital library development stages (collection building, indexing and access, use), and different analysis approaches (citation analysis, topic modelling, linking).
The call for papers attracted submissions from 29 countries on four continents. The program committee reviewed and accepted 26 full research papers (from 71 reviewed), 13 short research papers (from 38 reviewed), 4 tutorials, 5 workshops (from 7 reviewed), 45 posters and 3 demonstrations (from 68 reviewed). The doctoral consortium, which looks to assist and mentor young scholars in the investigation and research of digital libraries, received 20 submissions and accepted 11 for presentation at JCDL. This proceedings volume contains the full text of the papers, as well as abstracts of the keynotes, tutorials, workshops, panels, demonstrations, and posters.
All paper submissions went through a rigorous reviewing process with three individual reviewers on each paper and a meta-review by a fourth expert from the DL community, which prepared the discussion for the program committee meeting in February 2018. With over 160 PC committee members from three continents, the program committee met virtually to discuss all submitted papers and the conference schedule. Posters and demos were accepted in two rounds of submissions: first, in an open, public call, as well as a second, invitation-only round for converting longer submissions into poster form.
As in the past, we will be awarding three honors: the Vannevar Bush Best Paper Award, the Best Student Paper Award, and the Best Poster/Demo Award. During the opening session of the conference, the nominees for the two Best Paper Awards will be announced. The prizes will be presented at the banquet. We hope you will be inspired by the high quality and creativity of these award-winning papers.
We present a new recommender system developed for the Russian interactive radio network FMhost. To the best of our knowledge, it is the first model and associated case study for recommending radio stations hosted by real DJs rather than automatically built streamed playlists. To address such problems as cold start, gray sheep, boosting of rankings, preference and repertoire dynamics, and absence of explicit feedback, the underlying model combines a collaborative user-based approach with personalized information from tags of listened tracks in order to match user and radio station profiles. This is made possible with adaptive tag-aware profiling that follows an online learning strategy based on user history. We compare the proposed algorithms with singular value decomposition (SVD) in terms of precision, recall, and normalized discounted cumulative gain (NDCG) measures; experiments show that in our case the fusion-based approach demonstrates the best results. In addition, we give a theoretical analysis of some useful properties of fusion-based linear combination methods in terms of graded ordered sets.
This book constitutes the thoroughly refereed proceedings of the Third International Conference on Belief Functions, BELIEF 2014, held in Oxford, UK, in September 2014. The 47 revised full papers presented in this book were carefully selected and reviewed from 56 submissions. The papers are organized in topical sections on belief combination; machine learning; applications; theory; networks; information fusion; data association; and geometry.
A model for organizing cargo transportation between two node stations connected by a railway line which contains a certain number of intermediate stations is considered. The movement of cargo is in one direction. Such a situation may occur, for example, if one of the node stations is located in a region which produce raw material for manufacturing industry located in another region, and there is another node station. The organization of freight traﬃc is performed by means of a number of technologies. These technologies determine the rules for taking on cargo at the initial node station, the rules of interaction between neighboring stations, as well as the rule of distribution of cargo to the ﬁnal node stations. The process of cargo transportation is followed by the set rule of control. For such a model, one must determine possible modes of cargo transportation and describe their properties. This model is described by a ﬁnite-dimensional system of diﬀerential equations with nonlocal linear restrictions. The class of the solution satisfying nonlocal linear restrictions is extremely narrow. It results in the need for the “correct” extension of solutions of a system of diﬀerential equations to a class of quasi-solutions having the distinctive feature of gaps in a countable number of points. It was possible numerically using the Runge–Kutta method of the fourth order to build these quasi-solutions and determine their rate of growth. Let us note that in the technical plan the main complexity consisted in obtaining quasi-solutions satisfying the nonlocal linear restrictions. Furthermore, we investigated the dependence of quasi-solutions and, in particular, sizes of gaps (jumps) of solutions on a number of parameters of the model characterizing a rule of control, technologies for transportation of cargo and intensity of giving of cargo on a node station.
Event logs collected by modern information and technical systems usually contain enough data for automated process models discovery. A variety of algorithms was developed for process models discovery, conformance checking, log to model alignment, comparison of process models, etc., nevertheless a quick analysis of ad-hoc selected parts of a journal still have not get a full-fledged implementation. This paper describes an ROLAP-based method of multidimensional event logs storage for process mining. The result of the analysis of the journal is visualized as directed graph representing the union of all possible event sequences, ranked by their occurrence probability. Our implementation allows the analyst to discover process models for sublogs defined by ad-hoc selection of criteria and value of occurrence probability
Existing approaches suggest that IT strategy should be a reflection of business strategy. However, actually organisations do not often follow business strategy even if it is formally declared. In these conditions, IT strategy can be viewed not as a plan, but as an organisational shared view on the role of information systems. This approach generally reflects only a top-down perspective of IT strategy. So, it can be supplemented by a strategic behaviour pattern (i.e., more or less standard response to a changes that is formed as result of previous experience) to implement bottom-up approach. Two components that can help to establish effective reaction regarding new initiatives in IT are proposed here: model of IT-related decision making, and efficiency measurement metric to estimate maturity of business processes and appropriate IT. Usage of proposed tools is demonstrated in practical cases.