Negative Link Prediction in Social Media

Signed network analysis has attracted increasing attention in recent years. This is in part because research on signed network analysis suggests that negative links have added value in the analytical process. A major impediment in their effective use is that most social media sites do not enable users to specify them explicitly. In other words, a gap exists between the importance of negative links and their availability in real data sets. Therefore, it is natural to explore whether one can predict negative links automatically from the commonly available social network data. In this paper, we investigate the novel problem of negative link prediction with only positive links and content-centric interactions in social media. We make a number of important observations about negative links, and propose a principled framework NeLP, which can exploit positive links and content-centric interactions to predict negative links. Our experimental results on real-world social networks demonstrate that the proposed NeLP framework can accurately predict negative links with positive links and content-centric interactions. Our detailed experiments also illustrate the relative importance of various factors to the effectiveness of the proposed framework

Small Navies Do Have a Place in Network-Centric Warfare

In “Small Navies and Network-Centric Warfare” (Naval War College Review, Spring 2003, pp. 1–16), Paul T. Mitchell asked if there is a place for small navies in the world of network-centric warfare. From my perspective as the program executive officer for the U.S. Coast Guard’s Integrated Deepwater System (IDS), the answer is a resounding “Yes!” The price of admission, however, is a network- centric system for C4ISR (command, control, communications, computers, intelligence, surveillance, and reconnaissance), modern air and surface plat- forms, and a well-established relationship with the U.S. Navy