156 research outputs found
Discriminative Link Prediction using Local Links, Node Features and Community Structure
A link prediction (LP) algorithm is given a graph, and has to rank, for each
node, other nodes that are candidates for new linkage. LP is strongly motivated
by social search and recommendation applications. LP techniques often focus on
global properties (graph conductance, hitting or commute times, Katz score) or
local properties (Adamic-Adar and many variations, or node feature vectors),
but rarely combine these signals. Furthermore, neither of these extremes
exploit link densities at the intermediate level of communities. In this paper
we describe a discriminative LP algorithm that exploits two new signals. First,
a co-clustering algorithm provides community level link density estimates,
which are used to qualify observed links with a surprise value. Second, links
in the immediate neighborhood of the link to be predicted are not interpreted
at face value, but through a local model of node feature similarities. These
signals are combined into a discriminative link predictor. We evaluate the new
predictor using five diverse data sets that are standard in the literature. We
report on significant accuracy boosts compared to standard LP methods
(including Adamic-Adar and random walk). Apart from the new predictor, another
contribution is a rigorous protocol for benchmarking and reporting LP
algorithms, which reveals the regions of strengths and weaknesses of all the
predictors studied here, and establishes the new proposal as the most robust.Comment: 10 pages, 5 figure
Anomalies in the peer-review system: A case study of the journal of High Energy Physics
Peer-review system has long been relied upon for bringing quality research to
the notice of the scientific community and also preventing flawed research from
entering into the literature. The need for the peer-review system has often
been debated as in numerous cases it has failed in its task and in most of
these cases editors and the reviewers were thought to be responsible for not
being able to correctly judge the quality of the work. This raises a question
"Can the peer-review system be improved?" Since editors and reviewers are the
most important pillars of a reviewing system, we in this work, attempt to
address a related question - given the editing/reviewing history of the editors
or re- viewers "can we identify the under-performing ones?", with citations
received by the edited/reviewed papers being used as proxy for quantifying
performance. We term such review- ers and editors as anomalous and we believe
identifying and removing them shall improve the performance of the peer- review
system. Using a massive dataset of Journal of High Energy Physics (JHEP)
consisting of 29k papers submitted between 1997 and 2015 with 95 editors and
4035 reviewers and their review history, we identify several factors which
point to anomalous behavior of referees and editors. In fact the anomalous
editors and reviewers account for 26.8% and 14.5% of the total editors and
reviewers respectively and for most of these anomalous reviewers the
performance degrades alarmingly over time.Comment: 25th ACM International Conference on Information and Knowledge
Management (CIKM 2016
Correlations in complex networks under attack
For any initial correlated network after any kind of attack where either
nodes or edges are removed, we obtain general expressions for the degree-degree
probability matrix and degree distribution. We show that the proposed
analytical approach predicts the correct topological changes after the attack
by comparing the evolution of the assortativity coefficient for different
attack strategies and intensities in theory and simulations. We find that it is
possible to turn an initial assortative network into a disassortative one, and
vice versa, by fine-tuning removal of either nodes or edges. For an initial
uncorrelated network, on the other hand, we discover that only a targeted
edge-removal attack can induce such correlations
Stop Clickbait: Detecting and Preventing Clickbaits in Online News Media
Most of the online news media outlets rely heavily on the revenues generated
from the clicks made by their readers, and due to the presence of numerous such
outlets, they need to compete with each other for reader attention. To attract
the readers to click on an article and subsequently visit the media site, the
outlets often come up with catchy headlines accompanying the article links,
which lure the readers to click on the link. Such headlines are known as
Clickbaits. While these baits may trick the readers into clicking, in the long
run, clickbaits usually don't live up to the expectation of the readers, and
leave them disappointed.
In this work, we attempt to automatically detect clickbaits and then build a
browser extension which warns the readers of different media sites about the
possibility of being baited by such headlines. The extension also offers each
reader an option to block clickbaits she doesn't want to see. Then, using such
reader choices, the extension automatically blocks similar clickbaits during
her future visits. We run extensive offline and online experiments across
multiple media sites and find that the proposed clickbait detection and the
personalized blocking approaches perform very well achieving 93% accuracy in
detecting and 89% accuracy in blocking clickbaits.Comment: 2016 IEEE/ACM International Conference on Advances in Social Networks
Analysis and Mining (ASONAM
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