409 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
Learning and Forecasting Opinion Dynamics in Social Networks
Social media and social networking sites have become a global pinboard for
exposition and discussion of news, topics, and ideas, where social media users
often update their opinions about a particular topic by learning from the
opinions shared by their friends. In this context, can we learn a data-driven
model of opinion dynamics that is able to accurately forecast opinions from
users? In this paper, we introduce SLANT, a probabilistic modeling framework of
opinion dynamics, which represents users opinions over time by means of marked
jump diffusion stochastic differential equations, and allows for efficient
model simulation and parameter estimation from historical fine grained event
data. We then leverage our framework to derive a set of efficient predictive
formulas for opinion forecasting and identify conditions under which opinions
converge to a steady state. Experiments on data gathered from Twitter show that
our model provides a good fit to the data and our formulas achieve more
accurate forecasting than alternatives
Differentially Private Link Prediction With Protected Connections
Link prediction (LP) algorithms propose to each node a ranked list of nodes
that are currently non-neighbors, as the most likely candidates for future
linkage. Owing to increasing concerns about privacy, users (nodes) may prefer
to keep some of their connections protected or private. Motivated by this
observation, our goal is to design a differentially private LP algorithm, which
trades off between privacy of the protected node-pairs and the link prediction
accuracy. More specifically, we first propose a form of differential privacy on
graphs, which models the privacy loss only of those node-pairs which are marked
as protected. Next, we develop DPLP , a learning to rank algorithm, which
applies a monotone transform to base scores from a non-private LP system, and
then adds noise. DPLP is trained with a privacy induced ranking loss, which
optimizes the ranking utility for a given maximum allowed level of privacy
leakage of the protected node-pairs. Under a recently-introduced latent node
embedding model, we present a formal trade-off between privacy and LP utility.
Extensive experiments with several real-life graphs and several LP heuristics
show that DPLP can trade off between privacy and predictive performance more
effectively than several alternatives
Strain-induced stabilization of Al functionalization in graphene oxide nanosheet for enhanced NH3 storage
Strain effects on the stabilization of Al ad-atom on graphene
oxide(GO)nanosheet as well as its implications for NH3 storage have been
investigated using first-principles calculations.The binding energy of Al
ad-atom on GO is found to be a false indicator of its stability.Tensile strain
is found to be very effective in stabilizing the Al ad-atom on GO.It
strengthens the C-O bonds through an enhanced charge transfer from C to O
atoms. Interestingly,C-O bond strength is found to be the correct index for
Al's stability.Optimally strained Al-functionalized GO binds up to 6 NH3
molecules,while it binds no NH3 molecule in unstrained condition.Comment: 11 pages, 3 figures, 4 tables, Applied Physics Letters (Under Review
Generator Assisted Mixture of Experts For Feature Acquisition in Batch
Given a set of observations, feature acquisition is about finding the subset
of unobserved features which would enhance accuracy. Such problems have been
explored in a sequential setting in prior work. Here, the model receives
feedback from every new feature acquired and chooses to explore more features
or to predict. However, sequential acquisition is not feasible in some settings
where time is of the essence. We consider the problem of feature acquisition in
batch, where the subset of features to be queried in batch is chosen based on
the currently observed features, and then acquired as a batch, followed by
prediction. We solve this problem using several technical innovations. First,
we use a feature generator to draw a subset of the synthetic features for some
examples, which reduces the cost of oracle queries. Second, to make the feature
acquisition problem tractable for the large heterogeneous observed features, we
partition the data into buckets, by borrowing tools from locality sensitive
hashing and then train a mixture of experts model. Third, we design a tractable
lower bound of the original objective. We use a greedy algorithm combined with
model training to solve the underlying problem. Experiments with four datasets
show that our approach outperforms these methods in terms of trade-off between
accuracy and feature acquisition cost.Comment: Accepted in AAAI-2
Adversarial Permutation Guided Node Representations for Link Prediction
After observing a snapshot of a social network, a link prediction (LP)
algorithm identifies node pairs between which new edges will likely materialize
in future. Most LP algorithms estimate a score for currently non-neighboring
node pairs, and rank them by this score. Recent LP systems compute this score
by comparing dense, low dimensional vector representations of nodes. Graph
neural networks (GNNs), in particular graph convolutional networks (GCNs), are
popular examples. For two nodes to be meaningfully compared, their embeddings
should be indifferent to reordering of their neighbors. GNNs typically use
simple, symmetric set aggregators to ensure this property, but this design
decision has been shown to produce representations with limited expressive
power. Sequence encoders are more expressive, but are permutation sensitive by
design. Recent efforts to overcome this dilemma turn out to be unsatisfactory
for LP tasks. In response, we propose PermGNN, which aggregates neighbor
features using a recurrent, order-sensitive aggregator and directly minimizes
an LP loss while it is `attacked' by adversarial generator of neighbor
permutations. By design, PermGNN{} has more expressive power compared to
earlier symmetric aggregators. Next, we devise an optimization framework to map
PermGNN's node embeddings to a suitable locality-sensitive hash, which speeds
up reporting the top- most likely edges for the LP task. Our experiments on
diverse datasets show that \our outperforms several state-of-the-art link
predictors by a significant margin, and can predict the most likely edges fast.Comment: Rectified an error in evaluation in earlier 60-40 split
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