Finding influential users in microblogs: state-of-the-art methods and open research challenges

Author(s):  
Umar Ishfaq ◽  
Hikmat Ullah Khan ◽  
Shahid Iqbal ◽  
Mohammed Alghobiri
2021 ◽  
Vol 16 (2) ◽  
pp. 111-135
Author(s):  
Emilio M. Sanfilippo

Information entities are used in ontologies to represent engineering technical specifications, health records, pictures or librarian data about, e.g., narrative fictions, among others. The literature in applied ontology lacks a comparison of the state of the art, and foundational questions on the nature of information entities remain open for research. The purpose of the paper is twofold. First, to compare existing ontologies with both each other and theories proposed in philosophy, semiotics, librarianship, and literary studies in order to understand how the ontologies conceive and model information entities. Second, to discuss some open research challenges that can lead to principled approaches for the treatment of information entities, possibly by getting into account the variety of information entity types found in the literature.


2016 ◽  
Vol 23 (5) ◽  
pp. 10-16 ◽  
Author(s):  
Ejaz Ahmed ◽  
Ibrar Yaqoob ◽  
Abdullah Gani ◽  
Muhammad Imran ◽  
Mohsen Guizani

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 162008-162024 ◽  
Author(s):  
Mohamed Elhag Mohamed Abo ◽  
Ram Gopal Raj ◽  
Atika Qazi

2021 ◽  
Vol 54 (5) ◽  
pp. 1-34
Author(s):  
Maya Dotan ◽  
Yvonne-Anne Pignolet ◽  
Stefan Schmid ◽  
Saar Tochner ◽  
Aviv Zohar

Blockchains, in general, and cryptocurrencies such as Bitcoin, in particular, are realized using distributed systems and hence critically rely on the performance and security of the interconnecting network. The requirements on these networks and their usage, however, can differ significantly from traditional communication networks, with implications on all layers of the protocol stack. This article is motivated by these differences and, in particular, by the observation that many fundamental design aspects of these networks are not well-understood today. To support the networking community to contribute to this emerging application domain, we present a structured overview of the field, from topology and neighbor discovery, over block and transaction propagation, to sharding and off-chain networks, also reviewing existing empirical results from different measurement studies. In particular, for each of these domains, we provide the context, highlighting differences and commonalities with traditional networks, review the state-of-the-art, and identify open research challenges. Our article can hence also be seen as a call-to-arms to improve the foundation on top of which blockchains are built.


Author(s):  
Maria K. Krommyda ◽  
Verena Kantere

Large datasets pertaining to many scientific fields and everyday activities are becoming available at an increasing rate. Processing, analyzing, and understanding the information that they offer poses significant technical challenges. There are many efforts dedicated to the development of big data exploration, analysis, and visualization applications that will improve the value of the information extracted from these datasets. An analysis of the state-of-the-art in these applications is presented here along with open research challenges that have not yet been tackled sufficiently. Also, specific domains where big data applications are needed are presented, and unique challenges are identified.


2015 ◽  
Vol 30 (4) ◽  
pp. 435-453 ◽  
Author(s):  
Juan A. Rodriguez-Aguilar ◽  
Carles Sierra ◽  
Josep Ll. Arcos ◽  
Maite Lopez-Sanchez ◽  
Inmaculada Rodriguez

AbstractCoordination infrastructures play a central role in the engineering of multiagent systems. Since the advent of agent technology, research on coordination infrastructures has produced a significant number of infrastructures with varying features. In this paper, we review the the state-of-the-art coordination infrastructures with the purpose of identifying open research challenges that next generation coordination infrastructures should address. Our analysis concludes that next generation coordination infrastructures must address a number of challenges: (i) to becomesocially aware, by facilitating human interaction within a MAS; (ii) to assist agents in their decision making by providingdecision supportthat helps them reduce the scope of reasoning and facilitates the achievement of their goals; and (iii) to increaseopennessto support on-line, fully decentralised design and execution. Furthermore, we identify some promising approaches in the literature, together with the research issues worth investigating, to cope with such challenges.


Author(s):  
Viet Huynh ◽  
Dinh Phung ◽  
He Zhao

Optimal transport has a long history in mathematics which was proposed by Gaspard Monge in the eighteenth century (Monge, 1781). However, until recently, advances in optimal transport theory pave the way for its use in the AI community, particularly for formulating deep generative models. In this paper, we provide a comprehensive overview of the literature in the field of deep generative models using optimal transport theory with an aim of providing a systematic review as well as outstanding problems and more importantly, open research opportunities to use the tools from the established optimal transport theory in the deep generative model domain.


2018 ◽  
Vol 7 (4) ◽  
pp. 603-622 ◽  
Author(s):  
Leonardo Gutiérrez-Gómez ◽  
Jean-Charles Delvenne

Abstract Several social, medical, engineering and biological challenges rely on discovering the functionality of networks from their structure and node metadata, when it is available. For example, in chemoinformatics one might want to detect whether a molecule is toxic based on structure and atomic types, or discover the research field of a scientific collaboration network. Existing techniques rely on counting or measuring structural patterns that are known to show large variations from network to network, such as the number of triangles, or the assortativity of node metadata. We introduce the concept of multi-hop assortativity, that captures the similarity of the nodes situated at the extremities of a randomly selected path of a given length. We show that multi-hop assortativity unifies various existing concepts and offers a versatile family of ‘fingerprints’ to characterize networks. These fingerprints allow in turn to recover the functionalities of a network, with the help of the machine learning toolbox. Our method is evaluated empirically on established social and chemoinformatic network benchmarks. Results reveal that our assortativity based features are competitive providing highly accurate results often outperforming state of the art methods for the network classification task.


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