Signed Social Networks: A Survey

Girdhar, Nancy; Bharadwaj, Kamal Kant

doi:10.1007/978-981-10-5427-3_35

Cited by 15 publications

(6 citation statements)

References 27 publications

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“…On the other hand, other type of interactions could be of a "negative" nature, such as enmity, social exclusion, etc. Typically these two types of interactions are represented by assigning a sign to the edges of the social network (Girdhar & Bharadwaj, 2017), e.g., positive for social inclusion, and negative for social exclusion. It has been found (Schmälzle et al, 2017) that the social brain register the differences between social inclusion and social exclusion via an increased connectivity within a set of regions previously identified as a mentalizing system during exclusion relative to inclusion.…”

Section: Definition 3 Let a And B Be Two Elements Of A Set Of Entitie...mentioning

confidence: 99%

What is a Complex System, After All?

Estrada

2023

Found Sci

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The study of complex systems, although an interdisciplinary endeavor, is considered as an integrating part of physical sciences. Contrary to the historical fact that the field is already mature, it still lacks a clear and unambiguous definition of its main object of study. Here, I propose a definition of complex systems based on the conceptual clarifications made by Edgar Morin about the bidirectional non-separability of parts and whole produced by the nature of interactions. Then, a complex system is defined as the system where there is a bidirectional non-separability between the identities of the parts and the identity of the whole. Thus, not only the identity of the whole is determined by the constituent parts, but also the identity of the parts are determined by the whole due to the nature of their interactions. This concept allows, as shown in the paper, to derive some of the main properties that such systems must have as well as to propose its mathematical formalization.

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Section: Definition 3 Let a And B Be Two Elements Of A Set Of Entitie...mentioning

confidence: 99%

What is a Complex System, After All?

Estrada

2023

Found Sci

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“…In dividuals maintain positive affections as well as negative affections relative to one another, so social network functions as a platform to exhibit this type of relationship, whether healthy or unhealthy, friendly or hostile, like or unlike, belief or disbelief. These kinds of relationships give rise to the appearance of signed social networks (SSNs) in which positive sign means like a friend, trust, and negative sign presents dislike, rival, and distrust 29 . That is, social networks contain information regarding the positive links or negative links that highly influence the establishment of the future structure of the network.…”

Section: Related Workmentioning

confidence: 99%

“…These kinds of relationships give rise to the appearance of signed social networks (SSNs) in which positive sign means like a friend, trust, and negative sign presents dislike, rival, and distrust. 29 That is, social networks contain information regarding the positive links or negative links that highly influence the establishment of the future structure of the network. However, most of the social webs considered as possessing only friendly (positive) relationships, whereas neglecting the hostile (negative) ones entirely.…”

Section: Related Workmentioning

confidence: 99%

Supervised link prediction using structured‐based feature extraction in social network

Kumari

Behera

Sahoo

et al. 2020

Concurrency and Computation

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Summary Social network analysis (SNA) has attracted a lot of attention in several domains in the past decades. It can be of 2‐folds: one is content‐based, and another one is structured‐based analysis. Link prediction is one of the emerging research problems, which comes under structured‐based analysis that deals with predicting the missing link, which is likely to appear in the future. In this article, the supervised machine learning techniques have been implemented to predict the possibilities of establishing the links in future. The major contribution in this article lies in feature construction from the topological structure of the network. Several structured‐based similarity measures have been considered for preparing the feature vector for each nonexisting links in the network. The performance of the proposed algorithm has been extensively validated by comparing with other link prediction algorithms using both real‐world and synthetic data sets.

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“…The sentiments such as friends or foes, agreements or disagreements, likes or dislikes, trust or distrust, group joining or departing, and so on can be bi-categorized into positive connections such as friends, agreements, or likes and negative connections such as foes, disagreements, or dislikes. Such interactions yield to the development of Signed Social Networks (SSNs) [11], [12]. Certainly, SM signed networks are noisy and sparse usually with a massive number of users and multitudes of relationships.…”

Section: E: Signed Social Networkmentioning

confidence: 99%

Towards Deep Learning Prospects: Insights for Social Media Analytics

et al. 2019

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Deep learning (DL) has attracted increasing attention on account of its significant processing power in tasks, such as speech, image, or text processing. In order to the exponential development and widespread availability of digital social media (SM), analyzing these data using traditional tools and technologies is tough or even intractable. DL is found as an appropriate solution to this problem. In this paper, we keenly discuss the practiced DL architectures by presenting a taxonomy-oriented summary, following the major efforts made toward the SM analytics (SMA). Nevertheless, instead of the technical description, this paper emphasis on describing the SMA-oriented problems with the DL-based solutions. To this end, we also highlight the DL research challenges (such as scalability, heterogeneity, and multimodality) and future trends. INDEX TERMS Social media data, dynamic network, deep learning, feature learning. I. INTRODUCTION

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Signed Social Networks: A Survey

Cited by 15 publications

References 27 publications

What is a Complex System, After All?

What is a Complex System, After All?

Supervised link prediction using structured‐based feature extraction in social network

Towards Deep Learning Prospects: Insights for Social Media Analytics

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