Like like alike---Joint Friendship and Interest Propagation in Social Networks

by Shuang Hong Yang, Bo Long, Alex Smola, Narayanan Sadagopan, Zhaohui Zheng and Hongyuan Zha

This paper extends the idea of RLFM: we interpret RLFM with a supervised learning problem and this paper extends this idea with a social net, mainly via regularizers. So that's why the probabilistic model in RLFM may not be that important (just an interpretation in a disciplined way). There are 7 regularizers in total, which leads to a very complicated objective function.

The optimization is solved via stochastic gradient descent. Maybe for other techniques like interior point method, it is too difficult to derive?

Inferring Rlevent Social Networks from Interpersonal Communication

by Munmum de Choudhury, Winter A. Mason, Jake M. Hofman and Ducan J. Watts

The paper makes a point that the social nets are not really totally usable by its original form. We usually have to remove some edges by a certain extent for a specific tasks. My thought on this is like the social edges are like aqueduct or wires. Different behaviors have different propagation coefficients.

Another important thing in this paper is those features, in three categories:

• reach
  • node degree
  • average neighbor degree
  • size of two-hop neighborhood
• closure
  • embeddedness, an average of ratios of common friends over or-friends;
  • normalized clustering coefficient, the average probability of two of my neighbors are friends
• bridge
  • network constraints (sum of all neighbors, each inner producted with the node)
  • ego components (after removal, how many new components will appear)