Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: On the Troll-Trust Model for Edge Sign Prediction in Social Networks   In the problem of edge sign prediction, we are given a directed graph
(representing a social network), and our task is to predict the binary labels
of the edges (i.e., the positive or negative nature of the social
relationships). Many successful heuristics for this problem are based on the
troll-trust features, estimating at each node the fraction of outgoing and
incoming positive/negative edges. We show that these heuristics can be
understood, and rigorously analyzed, as approximators to the Bayes optimal
classifier for a simple probabilistic model of the edge labels. We then show
that the maximum likelihood estimator for this model approximately corresponds
to the predictions of a Label Propagation algorithm run on a transformed
version of the original social graph. Extensive experiments on a number of
real-world datasets show that this algorithm is competitive against
state-of-the-art classifiers in terms of both accuracy and scalability.
Finally, we show that troll-trust features can also be used to derive online
learning algorithms which have theoretical guarantees even when edges are
adversarially labeled.

24
The categories of this paper are: dg-ga
9
The categories of this paper are: nlin
31
The categories of this paper are: ao-sci
11
The categories of this paper are: hep-lat
35
The categories of this paper are: mtrl-th
29
The categories of this paper are: funct-an
16
The categories of this paper are: stat
15
The categories of this paper are: nucl-ex
7
The categories of this paper are: hep-th
28
The categories of this paper are: alg-geom
37
The categories of this paper are: supr-con
6
The categories of this paper are: astro-ph
17
The categories of this paper are: q-fin
26
The categories of this paper are: bayes-an
22
The categories of this paper are: chao-dyn
25
The categories of this paper are: patt-sol