Line graphs as social networks

The line graphs are clustered and assortative. They share these topological features with some social networks. We argue that this similarity reveals the cliquey character of the social networks. In the model proposed here, a social network is the line graph of an initial network of families, communities, interest groups, school classes and small companies. These groups play the role of nodes, and individuals are represented by links between these nodes. The picture is supported by the data on the LiveJournal network of about 8 x 10^6 people. In particular, sharp maxima of the observed data of the degree dependence of the clustering coefficient C(k) are associated with cliques in the social network.Comment: 11 pages, 4 figure

Reading the Source Code of Social Ties

Though online social network research has exploded during the past years, not much thought has been given to the exploration of the nature of social links. Online interactions have been interpreted as indicative of one social process or another (e.g., status exchange or trust), often with little systematic justification regarding the relation between observed data and theoretical concept. Our research aims to breach this gap in computational social science by proposing an unsupervised, parameter-free method to discover, with high accuracy, the fundamental domains of interaction occurring in social networks. By applying this method on two online datasets different by scope and type of interaction (aNobii and Flickr) we observe the spontaneous emergence of three domains of interaction representing the exchange of status, knowledge and social support. By finding significant relations between the domains of interaction and classic social network analysis issues (e.g., tie strength, dyadic interaction over time) we show how the network of interactions induced by the extracted domains can be used as a starting point for more nuanced analysis of online social data that may one day incorporate the normative grammar of social interaction. Our methods finds applications in online social media services ranging from recommendation to visual link summarization.Comment: 10 pages, 8 figures, Proceedings of the 2014 ACM conference on Web (WebSci'14

The Creation of an Arabic Emotion Ontology Based on E-Motive

© 2017 The Authors. Published by Elsevier B.V. There is an increased interest in social media monitoring to analyse massive, free form, short user-generated text from multiple social media sites such as Facebook, WhatsApp and Twitter. Companies are interested in sentiment analysis to understand customers\u27 opinions about their products/services. Governments and law enforcement agencies are interested in identifying threats to safeguard their country\u27s national security. They are actively seeking ways to monitor and analyse the public\u27s responses to various services, activities and events, especially since social media has become a valuable real-time resource of information. This study builds on prior work that focused on sentiment classification (i.e., positive, negative). This study primarily aims to design and develop a social sentiment-parsing algorithm for capturing and monitoring an extensive and comprehensive range of emotions from Arabic social media text. The study contributes to the field of sentiment analysis (opinion mining) and can subsequently be used for web mining, cleansing and analytics

Labeling Faces Victimization Bunch Primarily Based Internet Pictures Annotation to Produce Authentication in Security

Auto face annotation is important in abounding absolute apple advice administration systems. Face tagging in images and videos enjoys abounding abeyant applications in multimedia advice retrieval. Face comment is a meadow of face apprehension and recognition. Mining abominably labeled facial images on the internet shows abeyant classic appear auto face annotation. This blazon of classic motivates the new assay botheration of defended authentication. The ambition of the arrangement is to comment disregarded faces in images and videos with the words that best alarm the image. A framework called seek based face comment (SBFA) provides the way to abundance abominably labeled facial images. Facial images that are accessible on Apple Wide Web (WWW) or the angel database created by the aegis administration can be annotated. A one arduous botheration with the seek based face comment arrangement is how finer accomplish comment by advertisement agnate facial images and their anemic labels which are blatant and incomplete. To affected this botheration proposed admission uses unsupervised characterization clarification (ULR) to clarify the labels of web facial images. To acceleration up the proposed arrangement a absorption based approximation algorithm is used. Uses of comment will advice for user to seek admiration angel and video. As well if arrangement gets implemented in amusing arrangement again it will affected the check of accepted absolute arrangement which tags manually

Pattern recognition in narrative: Tracking emotional expression in context

Using geometric data analysis, our objective is the analysis of narrative, with narrative of emotion being the focus in this work. The following two principles for analysis of emotion inform our work. Firstly, emotion is revealed not as a quality in its own right but rather through interaction. We study the 2-way relationship of Ilsa and Rick in the movie Casablanca, and the 3-way relationship of Emma, Charles and Rodolphe in the novel {\em Madame Bovary}. Secondly, emotion, that is expression of states of mind of subjects, is formed and evolves within the narrative that expresses external events and (personal, social, physical) context. In addition to the analysis methodology with key aspects that are innovative, the input data used is crucial. We use, firstly, dialogue, and secondly, broad and general description that incorporates dialogue. In a follow-on study, we apply our unsupervised narrative mapping to data streams with very low emotional expression. We map the narrative of Twitter streams. Thus we demonstrate map analysis of general narratives