Social Network Analysis

Networked: the new social operating system

Medical cyber-physical systems (MCPS) are healthcare critical integration of a network of medical devices. These systems are progressively used in hospitals to achieve a continuous high-quality healthcare. The MCPS design faces numerous challenges, including inoperability, security/privacy, and high assurance in the system software. In the current work, the infrastructure of the cyber-physical systems (CPS) are reviewed and discussed. This article enriched the researches of the networked Medical Device (MD) systems to increase the efficiency and safety of the healthcare. It also can assist the specialists of medical device to overcome crucial issues related to medical devices, and the challenges facing the design of the medical device's network. The concept of the social networking and its security along with the concept of the wireless sensor networks (WSNs) are addressed. Afterward, the CPS systems and platforms have been established, where more focus was directed toward CPS-based healthcare. The big data framework of CPSs is also included.

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Medical cyber-physical systems: A survey.

The rapid proliferation of smart mobile devices has significantly enhanced the popularization of the cyber-physical social network, where users actively publish data with sensitive information. Adversaries can easily obtain these data and launch continuous attacks to breach privacy. However, existing works only focus on either location privacy or identity privacy with a static adversary. This results in privacy leakage and possible further damage. Motivated by this, we propose a hybrid privacy-preserving scheme, which considers both location and identity privacy against a dynamic adversary. We study the privacy protection problem as the tradeoff between the users aiming at maximizing data utility with high-level privacy protection while adversaries possessing the opposite goal. We first establish a game-based Markov decision process model, in which the user and the adversary are regarded as two players in a dynamic multistage zero-sum game. To acquire the best strategy for users, we employ a modified state-action-reward-state-action reinforcement learning algorithm. Iteration times decrease because of cardinality reduction from $n$ to 2, which accelerates the convergence process. Our extensive experiments on real-world data sets demonstrate the efficiency and feasibility of the propose method.

A Hybrid Privacy Protection Scheme in Cyber-Physical Social Networks

Friend recommendation system (FRS) is an essential part of any social network system. With the popularity of social network sites, many FRSs have been proposed in the past few years. However, most of them are homophily based systems, homophily is the propensity to associate and bond with similar others. In other words, these systems will recommend people that you share common features with them as friends. Homophily based FRS is accurate when the common feature is a physical or social feature, such as age, race, location, job, or lifestyle. However, it is not the case with personality types. Having a given personality type does not necessarily mean that you are compatible with people that have the same personality type. Therefore, in this paper, we present and evaluate an FRS based on the big-five personality traits model and hybrid filtering, in which the friend recommended process is based on personality traits and users’ harmony rating. To validate the proposed system’s accuracy, a personality-based social network site that uses the proposed FRS named PersoNet is implemented. Users’ rating results show that PersoNet performs better than collaborative filtering (CF)-based FRS in terms of precision and recall.

PersoNet: Friend Recommendation System Based on Big-Five Personality Traits and Hybrid Filtering

While privacy preserving mechanisms, such as hiding one’s friends list, may be available to withhold personal information on online social networking sites, it is not obvious whether to which degree a user’s social behavior renders such an attempt futile. In this paper, we study the impact of additional interaction information on the inference of links between nodes in partially covert networks. This investigation is based on the assumption that interaction might be a proxy for connectivity patterns in online social networks. For this purpose, we use data collected from 586 Facebook profiles consisting of friendship ties (conceptualized as the network) and comments on wall posts (serving as interaction information) by a total of 64 000 users. The link-inference problem is formulated as a binary classification problem using a comprehensive set of features and multiple supervised learning algorithms. Our results suggest that interactions reiterate the information contained in friendship ties sufficiently well to serve as a proxy when the majority of a network is unobserved.

Investigating Link Inference in Partially Observable Networks: Friendship Ties and Interaction

Cet article decrit la diversite des comportements sur Facebook. A partir d’une enquete quantitative portant sur les donnees extraites de 15 145 comptes Facebook, il propose une interpretation morphologique et structurale des modalites d’expression et d’interaction sur le reseau socio-numerique. Six configurations sont identifiees a partir des differentes activites que la plateforme offre aux utilisateurs : une classe de non-actifs, deux classes dominees par la conversation (en groupe, ou distribuee sur la page des amis) et trois classes d’utilisateurs qui privilegient l’expression sur leur propre page (egocentres, egovisibles, partageurs). En croisant ces donnees d’activite avec des indicateurs socio-demographiques ou structurels, on saisit l’influence capitale de l’âge, du sexe ou de la structure du reseau amical sur ces configurations. On observe ainsi une forte sensibilite du reseau en fonction de l’âge de l’utilisateur et une specialisation d’un sous-reseau de commentateurs reguliers chez les utilisateurs les plus actifs de Facebook.

https://hal.archives-ouvertes.fr/hal-01594564/document

Facebook, pour quoi faire ?. Configurations d’activités et structures relationnelles

Network science gathers methods coming from various disciplines which sometimes hardly cross the boundaries between these disciplines. Widely used in molecular biology in the study of protein interac- tion networks, the enumeration, in a network, of all possible subgraphs of a limited size (usually around four or five nodes), often called graphlets, can only be found in a few works dealing with social networks. In the present work, we apply this approach to an original corpus of about 10,000 non-overlapping Facebook ego networks gathered from voluntary participants by a survey application. To deal with so many simi- lar networks, we adapt the relative graphlet frequency to a measure that we call graphlet representativity, which we show to be more effective to classify random networks having slight structural differences. From our data, we produce two clusterings, one of graphlets (paths, star-like, holes, light triangles, and dense), one of networks. The latter is presented with a visualization scheme using our representativity measure. We describe the distinct structural characteristics of the five clusters of Facebook ego networks so obtained and discuss the empirical differences between results obtained with 4-node and 5-node graphlets. We also provide suggestions of follow-ups of this work, both in sociology and in network science.

/pdf/stars-holes-or-paths-across-your-facebook-friends-a-graphlet-2ni7j3jkgd.pdf

Stars, holes, or paths across your Facebook friends: A graphlet-based characterization of many networks

Network science gathers methods coming from various disciplines which sometimes hardly cross the boundaries between these disciplines. Widely used in molecular biology in the study of protein interaction networks, the enumeration, in a network, of all possible subgraphs of a limited size (usually around five nodes), often called graphlets, can only be found in a few works dealing with social networks. In the present work, we apply this approach to an original corpus of about 10 000 Facebook ego networks gathered from voluntary participants by a survey application. We define here an original measure that we call graphlet representativity, with which we produce a clustering of graphlets into five groups (paths, star-like, holes, light triangles, dense), along with an original visualization scheme for the comparison of ego networks. We then use the visu-alization in order to compare several clusterings of our corpus of networks, using various state-of-the art metrics. The graphlet representativity ends up producing the most dis-criminative clustering, so we describe the distinct structural characteristics of the five clusters of ego networks so obtained, and discuss the differences between 4-node and 5-node graphlets. We also provide many suggestions of followups of this work, both in sociology and in network science.

Graphlet-based characterization of many ego networks

There is a growing interest in how data generated in learning platforms, especially the interaction data, can be used to improve teaching and learning. Social network analysis and machine learning methods take advantage of network topology to detect relational patterns and model interaction behaviors. Specifically, small induced subgraphs called graphlets, provide an efficient topological description of the way each node is embedded in the meso-scale structure of a network. Here we propose to detect the roles occupied by the different participants, students and teachers, in the successive phases of courses modeled by a sequence of static snapshots. The detected positions, obtained thanks to graphlet enumeration combined with a clustering method, reveal the different roles observed in each snapshot. We also track the role changes through the overall sequence of snapshots. We apply our method to the Sqily platform and describe the mutual skill validation process. The detected roles, the transitions between roles and a overall visualization through Sankey diagrams help interpreting the course dynamics. We found that some roles act like necessary steps to engage students within an active exchange process with their classmates.

https://hal.archives-ouvertes.fr/hal-02317823/document

Raphaël Charbey

Papers

Investigating Link Inference in Partially Observable Networks: Friendship Ties and Interaction

Facebook, pour quoi faire ?. Configurations d’activités et structures relationnelles

Stars, holes, or paths across your Facebook friends: A graphlet-based characterization of many networks

Graphlet-based characterization of many ego networks

Roles in Social Interactions: Graphlets in Temporal Networks Applied to Learning Analytics.