scholarly journals Towards integrating real-world spatiotemporal data with social networks

2011 ◽  
Author(s):  
Huy Pham ◽  
Ling Hu ◽  
Cyrus Shahabi
Keyword(s):  
Social Networks ◽  
Real World ◽  
Spatiotemporal Data

Systems theories

2018 ◽  
Author(s):  
Marc J. Stern
Keyword(s):  
Social Networks ◽  
Problem Solving ◽  
Natural Resources ◽  
Real World ◽  
Common Pool Resources ◽  
Ecological Systems ◽  
Effective Governance ◽  
Social Ecological Systems ◽  
Social Ecological ◽  
Real World Problem

This chapter covers systems theories relevant to understanding and working to enhance the resilience of social-ecological systems. Social-ecological systems contain natural resources, users of those resources, and the interactions between each. The theories in the chapter share lessons about how to build effective governance structures for common pool resources, how to facilitate the spread of worthwhile ideas across social networks, and how to promote collaboration for greater collective impacts than any one organization alone could achieve. Each theory is summarized succinctly and followed by guidance on how to apply it to real world problem solving.

scholarly journals Exploiting Proximity-Based Mobile Apps for Large-Scale Location Privacy Probing

2018 ◽  
Vol 2018 ◽  
pp. 1-22 ◽  
Author(s):  
Shuang Zhao ◽  
Xiapu Luo ◽  
Xiaobo Ma ◽  
Bo Bai ◽  
Yankang Zhao ◽  
...  
Keyword(s):  
Social Networks ◽  
Case Studies ◽  
Real World ◽  
Emission Reduction ◽  
Large Scale ◽  
Location Privacy ◽  
Mobile Apps ◽  
Physical World ◽  
Typical Type ◽  
Location Spoofing

Proximity-based apps have been changing the way people interact with each other in the physical world. To help people extend their social networks, proximity-based nearby-stranger (NS) apps that encourage people to make friends with nearby strangers have gained popularity recently. As another typical type of proximity-based apps, some ridesharing (RS) apps allowing drivers to search nearby passengers and get their ridesharing requests also become popular due to their contribution to economy and emission reduction. In this paper, we concentrate on the location privacy of proximity-based mobile apps. By analyzing the communication mechanism, we find that many apps of this type are vulnerable to large-scale location spoofing attack (LLSA). We accordingly propose three approaches to performing LLSA. To evaluate the threat of LLSA posed to proximity-based mobile apps, we perform real-world case studies against an NS app named Weibo and an RS app called Didi. The results show that our approaches can effectively and automatically collect a huge volume of users’ locations or travel records, thereby demonstrating the severity of LLSA. We apply the LLSA approaches against nine popular proximity-based apps with millions of installations to evaluate the defense strength. We finally suggest possible countermeasures for the proposed attacks.

Direction-Aware User Recommendation Based on Asymmetric Network Embedding

2022 ◽  
Vol 40 (2) ◽  
pp. 1-23
Author(s):  
Sheng Zhou ◽  
Xin Wang ◽  
Martin Ester ◽  
Bolang Li ◽  
Chen Ye ◽  
...  
Keyword(s):  
Social Networks ◽  
Random Walk ◽  
Real World ◽  
Directed Network ◽  
Network Embedding ◽  
The Social ◽  
Benchmark Datasets ◽  
Direction Information ◽  
User Recommendation ◽  
Asymmetric Proximity

User recommendation aims at recommending users with potential interests in the social network. Previous works have mainly focused on the undirected social networks with symmetric relationship such as friendship, whereas recent advances have been made on the asymmetric relationship such as the following and followed by relationship. Among the few existing direction-aware user recommendation methods, the random walk strategy has been widely adopted to extract the asymmetric proximity between users. However, according to our analysis on real-world directed social networks, we argue that the asymmetric proximity captured by existing random walk based methods are insufficient due to the inbalance in-degree and out-degree of nodes. To tackle this challenge, we propose InfoWalk, a novel informative walk strategy to efficiently capture the asymmetric proximity solely based on random walks. By transferring the direction information into the weights of each step, InfoWalk is able to overcome the limitation of edges while simultaneously maintain both the direction and proximity. Based on the asymmetric proximity captured by InfoWalk, we further propose the qualitative (DNE-L) and quantitative (DNE-T) directed network embedding methods, capable of preserving the two properties in the embedding space. Extensive experiments conducted on six real-world benchmark datasets demonstrate the superiority of the proposed DNE model over several state-of-the-art approaches in various tasks.

scholarly journals Who comes to mind? Dynamic construction of social networks

2020 ◽  
Author(s):  
Joseph Bayer ◽  
Neil Anthony Lewis ◽  
Jonathan Stahl
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Real World ◽  
Dynamic Process ◽  
Social Groups ◽  
Social Resources ◽  
Personal Relationships ◽  
Cognitive Mechanisms ◽  
Relational Structures ◽  
Network Technologies

Much remains unknown about moment-to-moment social-network cognition — that is, who comes to mind as we go about our day-to-day lives. Responding to this void, we describe the real-time construction of cognitive social networks. First, we outline the types of relational structures that comprise momentary networks, distinguishing the roles of personal relationships, social groups, and mental sets. Second, we discuss the cognitive mechanisms that determine which individuals are activated — and which are neglected — through a dynamic process. Looking forward, we contend that these overlooked mechanisms need to be considered in light of emerging network technologies. Finally, we chart the next steps for understanding social-network cognition across real-world contexts, along with the built-in implications for social resources and intergroup disparities.

A comprehensive study of online event tracking algorithms in social networks

2018 ◽  
Vol 45 (2) ◽  
pp. 156-168 ◽  
Author(s):  
Mahsa Seifikar ◽  
Saeed Farzi
Keyword(s):  
Social Networks ◽  
Real World ◽  
Continuous Time ◽  
Trend Detection ◽  
Information Need ◽  
Open Problems ◽  
Advantages And Disadvantages ◽  
Event Tracking ◽  
Social Media Platforms ◽  
Comprehensive Study

Recently, social networks have provided an important platform to detect trends of real-world events. The trends of real-world events are detected by analysing flow of massive bulks of data in continuous time steps over various social media platforms. Today, many researchers have been interested in detecting social network trends, in order to analyse the gathered information for enabling users and organisations to satisfy their information need. This article is aimed at complete surveying the recent text-based trend detection approaches, which have been studied from three perspectives (algorithms, dimension and diversity of events). The advantages and disadvantages of the considered approaches have also been paraphrased separately to illustrate a comprehensive view of the previous works and open problems.

Graph Mining Techniques

2013 ◽  
pp. 446-464 ◽  
Author(s):  
Ana Paula Appel ◽  
Christos Faloutsos ◽  
Caetano Traina Junior
Keyword(s):  
Social Networks ◽  
Communication Networks ◽  
Real World ◽  
Biological Networks ◽  
Graph Mining ◽  
Recommendation Systems ◽  
Computer Communication Networks ◽  
Computer Communication ◽  
Good Pattern ◽  
As Graph

Graphs appear in several settings, like social networks, recommendation systems, computer communication networks, gene/protein biological networks, among others. A large amount of graph patterns, as well as graph generator models that mimic such patterns have been proposed over the last years. However, a deep and recurring question still remains: “What is a good pattern?” The answer is related to finding a pattern or a tool able to help distinguishing between actual real-world and fake graphs. Here we explore the ability of ShatterPlots, a simple and powerful algorithm to tease out patterns of real graphs, helping us to spot fake/masked graphs. The idea is to force a graph to reach a critical (“Shattering”) point, randomly deleting edges, and study its properties at that point.

Information Attacks and Defenses on the Social Web

2018 ◽  
pp. 216-235
Author(s):  
Agostino Poggi ◽  
Michele Tomaiuolo
Keyword(s):  
Social Networks ◽  
Social Networking ◽  
Real World ◽  
Web Sites ◽  
Online Social Networks ◽  
Social Web ◽  
Security Measures ◽  
The Social ◽  
Attacks And Defenses ◽  
Interest In Technology

Social web sites are used daily by many millions of users. They have attracted users with very weak interest in technology, including absolute neophytes of computers in general. Common users of social web sites often have a carefree attitude in sharing information. Moreover, some system operators offer sub-par security measures, which are not adequate for the high value of the published information. For all these reasons, online social networks suffer more and more attacks by sophisticated crackers and scammers. To make things worse, the information gathered from social web sites can trigger attacks to even more sensible targets. This work reviews some typical social attacks that are conducted on social networking systems, describing real-world examples of such violations and analyzing in particular the weakness of password mechanisms. It then presents some solutions that could improve the overall security of the systems.

Competence Management over Social Networks Through Dynamic Taxonomies

2013 ◽  
pp. 103-120
Author(s):  
Giuseppe Berio ◽  
Antonio Di Leva ◽  
Mounira Harzallah ◽  
Giovanni M. Sacco
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Real World ◽  
Reference Model ◽  
Effective Solution ◽  
Network Information ◽  
The Social ◽  
Competence Management

The exploitation and integration of social network information in a competence reference model (CRAI, Competence, Resource, Aspect, Individual) are discussed. The Social-CRAI model, which extends CRAI to social networks, provides an effective solution to this problem and is discussed in detail. Finally, dynamic taxonomies, a model supporting explorative conceptual search, are introduced and their use in the context of the Social-CRAI model for exploring retrieved information available in social networks is discussed. A real-world example is provided.

Dynamics and Evolutional Patterns of Social Networks

2011 ◽  
pp. 156-170
Author(s):  
Yingzi Jin ◽  
Yutaka Matsuo
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Network Analysis ◽  
Real World ◽  
Dynamic Network ◽  
Network Evolution ◽  
Belief Formation ◽  
Network Mining ◽  
Longitudinal Network Analysis ◽  
Relational Network

Previous chapters focused on the models of static networks, which consider a relational network at a given point in time. However, real-world social networks are dynamic in nature; for example, friends of friends become friends. Social network research has, in recent years, paid increasing attention to dynamic and longitudinal network analysis in order to understand network evolution, belief formation, friendship formation, and so on. This chapter focuses mainly on the dynamics and evolutional patterns of social networks. The chapter introduces real-world applications and reviews major theories and models of dynamic network mining.

Cityware

2010 ◽  
pp. 911-919 ◽  
Author(s):  
Vassilis Kostakos ◽  
Eamonn O’Neill
Keyword(s):  
Social Networks ◽  
Real World ◽  
Online Social Networks ◽  
Social Data

In this paper, we describe a platform that enables us to systematically study online social networks alongside their real-world counterparts. Our system, entitled Cityware, merges users’ online social data, made available through Facebook, with mobility traces captured via Bluetooth scanning. Furthermore, our system enables users to contribute their own mobility traces, thus allowing users to form and participate in a community. In addition to describing Cityware’s architecture, we discuss the type of data we are collecting, and the analyses our platform enables, as well as users’ reactions and thoughts.

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