Discovering Influential Nodes from Social Trust Network

The goal of viral marketing is that, by the virtue of mouth to mouth word spread, a small set of influential customers can influence greater number of customers. Influence maximization (IM) task is to discover such influential nodes (or customers) from a social network. Existing algorithms adopt Greedy based approaches, which assume only positive influence among users. But in real life network, such as trust network, one can also get negatively influenced. In this research we propose a model, called T-GT model, considering both positive and negative influence. To solve IM under this model, a trust network where relationships among users are either `trust\u27 or `distrust\u27 is considered. We first compute positive and negative influence by mining frequent patterns of actions performed. Then using local search a new algorithm, called MineSeedLS, is proposed. Experimental results on real trust network shows that our approach outperforms Greedy based approach by almost 35%

Identifying a Criminal's Network of Trust

Tracing criminal ties and mining evidence from a large network to begin a crime case analysis has been difficult for criminal investigators due to large numbers of nodes and their complex relationships. In this paper, trust networks using blind carbon copy (BCC) emails were formed. We show that our new shortest paths network search algorithm combining shortest paths and network centrality measures can isolate and identify criminals' connections within a trust network. A group of BCC emails out of 1,887,305 Enron email transactions were isolated for this purpose. The algorithm uses two central nodes, most influential and middle man, to extract a shortest paths trust network.Comment: 2014 Tenth International Conference on Signal-Image Technology & Internet-Based Systems (Presented at Third International Workshop on Complex Networks and their Applications,SITIS 2014, Marrakesh, Morocco, 23-27, November 2014

Identifying communicator roles in Twitter

Twitter has redefined the way social activities can be coordinated; used for mobilizing people during natural disasters, studying health epidemics, and recently, as a communication platform during social and political change. As a large scale system, the volume of data transmitted per day presents Twitter users with a problem: how can valuable content be distilled from the back chatter, how can the providers of valuable information be promoted, and ultimately how can influential individuals be identified?To tackle this, we have developed a model based upon the Twitter message exchange which enables us to analyze conversations around specific topics and identify key players in a conversation. A working implementation of the model helps categorize Twitter users by specific roles based on their dynamic communication behavior rather than an analysis of their static friendship network. This provides a method of identifying users who are potentially producers or distributers of valuable knowledge

On the discovery of social roles in large scale social systems

The social role of a participant in a social system is a label conceptualizing the circumstances under which she interacts within it. They may be used as a theoretical tool that explains why and how users participate in an online social system. Social role analysis also serves practical purposes, such as reducing the structure of complex systems to rela- tionships among roles rather than alters, and enabling a comparison of social systems that emerge in similar contexts. This article presents a data-driven approach for the discovery of social roles in large scale social systems. Motivated by an analysis of the present art, the method discovers roles by the conditional triad censuses of user ego-networks, which is a promising tool because they capture the degree to which basic social forces push upon a user to interact with others. Clusters of censuses, inferred from samples of large scale network carefully chosen to preserve local structural prop- erties, define the social roles. The promise of the method is demonstrated by discussing and discovering the roles that emerge in both Facebook and Wikipedia. The article con- cludes with a discussion of the challenges and future opportunities in the discovery of social roles in large social systems

Supporting social innovation through visualisations of community interactions

Online communities that form through the introduction of sociotechnical platforms require significant effort to cultivate and sustain. Providing open, transparent information on community behaviour can motivate participation from community members themselves, while also providing platform administrators with detailed interaction dynamics. However, challenges arise in both understanding what information is conducive to engagement and sustainability, and then how best to represent this information to platform stakeholders. Towards a better understanding of these challenges, we present the design, implementation, and evaluation of a set of simple visualisations integrated into a Collective Awareness Platform for Social Innovation platform titled commonfare.net. We discuss the promise and challenge of bringing social innovation into the digital age, in terms of supporting sustained platform use and collective action, and how the introduction of community visualisations has been directed towards achieving this goal

Blockchain Inefficiency in the Bitcoin Peers Network

We investigate Bitcoin network monitoring the dynamics of blocks and transactions. We unveil that 43\% of the transactions are still not included in the Blockchain after 1h from the first time they were seen in the network and 20\% of the transactions are still not included in the Blockchain after 30 days, revealing therefore great inefficiency in the Bitcoin system. However, we observe that most of these `forgotten' transactions have low values and in terms of transferred value the system is less inefficient with 93\% of the transactions value being included into the Blockchain within 3h. The fact that a sizeable fraction of transactions is not processed timely casts serious doubts on the usability of the Bitcoin Blockchain for reliable time-stamping purposes and calls for a debate about the right systems of incentives which a peer-to-peer unintermediated system should introduce to promote efficient transaction recording.Comment: 15 pages, 8 figures, 3 table