Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

In social networks, link prediction predicts missing links in current networks and new or dissolution links in future networks, is important for mining and analyzing the evolution of social networks. In the past decade, many works have been done about the link prediction in social networks. The goal of this paper is to comprehensively review, analyze and discuss the state-of-the-art of the link prediction in social networks. A systematical category for link prediction techniques and problems is presented. Then link prediction techniques and problems are analyzed and discussed. Typical applications of link prediction are also addressed. Achievements and roadmaps of some active research groups are introduced. Finally, some future challenges of the link prediction in social networks are discussed.

Link prediction in social networks: the state-of-the-art

The introduction of intelligent devices with short range wireless communication techniques has motivated the development of Mobile Ad hoc NETworks (MANETs) during the last few years. However, traditional end-to-end based routing algorithms designed for MANETs are not much robust in the challenged networks suffering from frequent disruption, sparse network density and limited device capability. Such challenged networks, also known as Intermittently Connected Networks (ICNs) adopt the Store-Carry-Forward (SCF) behavior arising from the mobility of mobile nodes for message relaying. In this article, we consider the term ICNs as Delay/Disruption Tolerant Networks (DTNs) for the purpose of generalization, since DTNs have been envisioned for different applications with a large number of proposed routing algorithms. Motivated by the great interest from the research community, we firstly review the existing unicasting issue of DTNs because of its extensive research stage. Then, we also address multicasting and anycasting issues in DTNs considering their perspectives. A detail survey based on our taxonomy over the period from 2006 to 2010 is not only provided but also a comparison is given. We further identify the remaining challenges and open issues followed by an evaluation framework proposed for routing in DTNs. Finally, we summarize our contribution with three future research topics highlighted.

Routing in Delay/Disruption Tolerant Networks: A Taxonomy, Survey and Challenges

The mobile social network (MSN) combines techniques in social science and wireless communications for mobile networking The MSN can be considered as a system which provides a variety of data delivery services involving the social relationship among mobile users This paper presents a comprehensive survey on the MSN specifically from the perspectives of applications, network architectures, and protocol design issues First, major applications of the MSN are reviewed Next, different architectures of the MSN are presented Each of these different architectures supports different data delivery scenarios The unique characteristics of social relationship in MSN give rise to different protocol design issues These research issues (eg, community detection, mobility, content distribution, content sharing protocols, and privacy) and the related approaches to address data delivery in the MSN are described At the end, several important research directions are outlined

Applications, Architectures, and Protocol Design Issues for Mobile Social Networks: A Survey

Mobile social networks (MSNs) are a kind of delay tolerant network that consists of lots of mobile nodes with social characteristics. Recently, many social-aware algorithms have been proposed to address routing problems in MSNs. However, these algorithms tend to forward messages to the nodes with locally optimal social characteristics, and thus cannot achieve the optimal performance. In this paper, we propose a distributed optimal Community-Aware Opportunistic Routing (CAOR) algorithm. Our main contributions are that we propose a home-aware community model, whereby we turn an MSN into a network that only includes community homes. We prove that, in the network of community homes, we can still compute the minimum expected delivery delays of nodes through a reverse Dijkstra algorithm and achieve the optimal opportunistic routing performance. Since the number of communities is far less than the number of nodes in magnitude, the computational cost and maintenance cost of contact information are greatly reduced. We demonstrate how our algorithm significantly outperforms the previous ones through extensive simulations, based on a real MSN trace and a synthetic MSN trace.

/pdf/community-aware-opportunistic-routing-in-mobile-social-1yp1f02fkt.pdf

Community-Aware Opportunistic Routing in Mobile Social Networks

Twitter as a new form of social media potentially contains useful information that opens new opportunities for content analysis on tweets. This paper examines the predictive power of Twitter regarding the US presidential election of 2012. For this study, we analyzed 32 million tweets regarding the US presidential election by employing a combination of machine learning techniques. We devised an advanced classifier for sentiment analysis in order to increase the accuracy of Twitter content analysis. We carried out our analysis by comparing Twitter results with traditional opinion polls. In addition, we used the Latent Dirichlet Allocation model to extract the underlying topical structure from the selected tweets. Our results show that we can determine the popularity of candidates by running sentiment analysis. We can also uncover candidates popularities in the US states by running the sentiment analysis algorithm on geo-tagged tweets. To the best of our knowledge, no previous work in the field has presented a systematic analysis of a considerable number of tweets employing a combination of analysis techniques by which we conducted this study. Thus, our results aptly suggest that Twitter as a well-known social medium is a valid source in predicting future events such as elections. This implies that understanding public opinions and trends via social media in turn allows us to propose a cost- and time-effective way not only for spreading and sharing information, but also for predicting future events.

The Predictive Power of Social Media: On the Predictability of U.S. Presidential Elections using Twitter.

Efficient routing in mobile opportunistic networks and Delay Tolerant Networks (DTNs) in particular, is a challenging task because human mobility patterns are hard to predict. Several recent work have shown the importance of communities in efficient routing of messages in DTNs. However, real time community detection in DTNs is a complex and time consuming process. In this paper, we propose a simple and cost effective method for bootstrapping wireless devices by employing available social profiles. Moreover, we propose a simple greedy routing algorithm called Social-Greedy which uses a social distance derived from people's social profiles to route messages in DTNs.

http://kazemjahanbakhsh.com/docs/MobiOpp10_sg.pdf

Social-Greedy: a socially-based greedy routing algorithm for delay tolerant networks

Fast track article: Predicting missing contacts in mobile social networks

Experimentally measured contact traces, such as those obtained in a conference setting by using short range wireless sensors, are usually limited with respect to the practical number of sensors that can be deployed as well as available human volunteers. Moreover, most previous experiments in this field are partial since not everyone participating in the experiment is expected to carry a sensor device. Previously collected contact traces have significantly contributed to development of more realistic human mobility models. This in turn has influenced proposed routing algorithms for Delay Tolerant Networks where human contacts play a vital role in message delivery. By exploiting time-spatial properties of contact graphs as well as popularity and social information of mobile nodes, we propose a novel method to reconstruct the missing parts of contact graphs where only a subset of nodes are able to sense human contacts.

http://kazemjahanbakhsh.com/docs/wowmom10_pred.pdf

Predicting missing contacts in mobile social networks

Predicting human mobility is considered as a challenging problem In this paper, we formulate the problem of human contact prediction as a graph inference problem We show the importance of using offline social information for predicting people's contacts motivated by homophily theory We also prove that by using the small-world network properties of the contact graphs, we can reconstruct the missing part of a contact graph where only part of the graph is known Our results are promising because they allow researchers to reconstruct the missing parts in experimentally measured human mobility traces when only partial traces are obtainable

http://www.kazemjahanbakhsh.com/docs/sn10_contact.pdf

Kazem Jahanbakhsh

Papers

The Predictive Power of Social Media: On the Predictability of U.S. Presidential Elections using Twitter.

Social-Greedy: a socially-based greedy routing algorithm for delay tolerant networks

Fast track article: Predicting missing contacts in mobile social networks

Predicting missing contacts in mobile social networks

Human Contact Prediction Using Contact Graph Inference