60,203 research outputs found
Search Efficient Binary Network Embedding
Traditional network embedding primarily focuses on learning a dense vector
representation for each node, which encodes network structure and/or node
content information, such that off-the-shelf machine learning algorithms can be
easily applied to the vector-format node representations for network analysis.
However, the learned dense vector representations are inefficient for
large-scale similarity search, which requires to find the nearest neighbor
measured by Euclidean distance in a continuous vector space. In this paper, we
propose a search efficient binary network embedding algorithm called BinaryNE
to learn a sparse binary code for each node, by simultaneously modeling node
context relations and node attribute relations through a three-layer neural
network. BinaryNE learns binary node representations efficiently through a
stochastic gradient descent based online learning algorithm. The learned binary
encoding not only reduces memory usage to represent each node, but also allows
fast bit-wise comparisons to support much quicker network node search compared
to Euclidean distance or other distance measures. Our experiments and
comparisons show that BinaryNE not only delivers more than 23 times faster
search speed, but also provides comparable or better search quality than
traditional continuous vector based network embedding methods
Minimizing Polarization and Disagreement in Social Networks
The rise of social media and online social networks has been a disruptive
force in society. Opinions are increasingly shaped by interactions on online
social media, and social phenomena including disagreement and polarization are
now tightly woven into everyday life. In this work we initiate the study of the
following question: given agents, each with its own initial opinion that
reflects its core value on a topic, and an opinion dynamics model, what is the
structure of a social network that minimizes {\em polarization} and {\em
disagreement} simultaneously?
This question is central to recommender systems: should a recommender system
prefer a link suggestion between two online users with similar mindsets in
order to keep disagreement low, or between two users with different opinions in
order to expose each to the other's viewpoint of the world, and decrease
overall levels of polarization? Our contributions include a mathematical
formalization of this question as an optimization problem and an exact,
time-efficient algorithm. We also prove that there always exists a network with
edges that is a approximation to the optimum.
For a fixed graph, we additionally show how to optimize our objective function
over the agents' innate opinions in polynomial time.
We perform an empirical study of our proposed methods on synthetic and
real-world data that verify their value as mining tools to better understand
the trade-off between of disagreement and polarization. We find that there is a
lot of space to reduce both polarization and disagreement in real-world
networks; for instance, on a Reddit network where users exchange comments on
politics, our methods achieve a -fold reduction in polarization
and disagreement.Comment: 19 pages (accepted, WWW 2018
Playing Stackelberg Opinion Optimization with Randomized Algorithms for Combinatorial Strategies
From a perspective of designing or engineering for opinion formation games in
social networks, the "opinion maximization (or minimization)" problem has been
studied mainly for designing subset selecting algorithms. We furthermore define
a two-player zero-sum Stackelberg game of competitive opinion optimization by
letting the player under study as the first-mover minimize the sum of expressed
opinions by doing so-called "internal opinion design", knowing that the other
adversarial player as the follower is to maximize the same objective by also
conducting her own internal opinion design.
We propose for the min player to play the "follow-the-perturbed-leader"
algorithm in such Stackelberg game, obtaining losses depending on the other
adversarial player's play. Since our strategy of subset selection is
combinatorial in nature, the probabilities in a distribution over all the
strategies would be too many to be enumerated one by one. Thus, we design a
randomized algorithm to produce a (randomized) pure strategy. We show that the
strategy output by the randomized algorithm for the min player is essentially
an approximate equilibrium strategy against the other adversarial player
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