Research: Risks of Friendships on Social Networks
Authors: Prepared by Cuneyt Gurcan Akcora, Barbara Carminati and Elena Ferrari (DISTA, Universita` degli Studi dell’Insubria Via Mazzini 5, Varese, Italy), Risks of Friendships on Social Networks is a prepared paper submitted and accepted by the 2012 IEEE Conference on Data Mining (ICDM).
Abstract: In this paper, the authors explore the risks of friends in social networks caused by their friendship patterns, by using real life social network data and starting from a previously defined risk model. Particularly, they observe that risks of friendships can be mined by analyzing users’ attitude towards friends of friends. This allows new insights into friendship and risk dynamics on social networks.
Analysis: Summarized analysis from this paper includes observations on:
Applicability: Risks of Friendships on Social Networks offers unique insight into the privacy risks of online friendships and provides salient considerations for the development of risk models that could be applied to social network users.
Access: (PDF) http://bit.ly/Xk5mlX (arXiv.org)
This entry was posted on Tuesday, February 19th, 2013 at 2:26 pm. It is filed under chronology, discover and tagged with research, social media. You can follow any responses to this entry through the RSS 2.0 feed.
Comments are closed.
An abridged look at the business of eDiscovery mergers, acquisitions, and investments. The presented listing highlights key industry business moves by sharing the announcement date, acquired company, acquiring or investing company, and acquisition amount (if known) of significant eDiscovery-related mergers, acquisitions, and investments.
Taken from a combination of public market sizing estimations as shared in leading electronic discovery reports, publications and posts over time, the following eDiscovery Market Size Mashup shares general worldwide market sizing considerations for software and services in the electronic discovery market for the years between 2016 and 2021.
One of the core purposes of all of the Tracks is to demonstrate the robustness of core retrieval technology. Moreover, one of the primary goals of TREC is: [T]o speed the transfer of technology from research labs into commercial products by demonstrating substantial improvements in retrieval methodologies on real-world problems.
The proceedings of the TREC Total Recall Track have been published by the National Institute of Standards and Technology. The purpose of track was to investigate methods and technologies to find, as nearly as possible, all documents in a collection that satisfy specific criteria, with reasonable effort.
Best Practices for eDiscovery Searching: A Continuing Legal Education (CLE) On-Demand Presentation (1.0 Hour) prepared and presented by CloudNine. This CLE-approved webcast session will cover goals for effective searching, what to consider prior to collecting ESI that will be subject to search, mechanisms for culling prior to searching, mechanisms for improving search recall and precision, challenges to effective searching and recommended best practices for searching and validating your search results to ensure effective search results.
This is the e-Discovery Team’s training course on how to do TAR (Technology Assisted Review). What TAR really means is electronic document review enhanced by active machine learning, a type of specialized Artificial Intelligence. Our method of AI-enhanced document review is called Hybrid Multimodal IST Predictive Coding 4.0. The Course is composed of sixteen classes.
ComplexDiscovery | Creative Commons Attribution 4.0 International
Updated 7/23/2013: Provided for your consideration and use are the in-progress results of the Predictive Coding and Provider Survey launched...