10,167 research outputs found
Predicting Diffusion Reach Probabilities via Representation Learning on Social Networks
Diffusion reach probability between two nodes on a network is defined as the
probability of a cascade originating from one node reaching to another node. An
infinite number of cascades would enable calculation of true diffusion reach
probabilities between any two nodes. However, there exists only a finite number
of cascades and one usually has access only to a small portion of all available
cascades. In this work, we addressed the problem of estimating diffusion reach
probabilities given only a limited number of cascades and partial information
about underlying network structure. Our proposed strategy employs node
representation learning to generate and feed node embeddings into machine
learning algorithms to create models that predict diffusion reach
probabilities. We provide experimental analysis using synthetically generated
cascades on two real-world social networks. Results show that proposed method
is superior to using values calculated from available cascades when the portion
of cascades is small
On the Convexity of Latent Social Network Inference
In many real-world scenarios, it is nearly impossible to collect explicit
social network data. In such cases, whole networks must be inferred from
underlying observations. Here, we formulate the problem of inferring latent
social networks based on network diffusion or disease propagation data. We
consider contagions propagating over the edges of an unobserved social network,
where we only observe the times when nodes became infected, but not who
infected them. Given such node infection times, we then identify the optimal
network that best explains the observed data. We present a maximum likelihood
approach based on convex programming with a l1-like penalty term that
encourages sparsity. Experiments on real and synthetic data reveal that our
method near-perfectly recovers the underlying network structure as well as the
parameters of the contagion propagation model. Moreover, our approach scales
well as it can infer optimal networks of thousands of nodes in a matter of
minutes.Comment: NIPS, 201
Influence Maximization with Bandits
We consider the problem of \emph{influence maximization}, the problem of
maximizing the number of people that become aware of a product by finding the
`best' set of `seed' users to expose the product to. Most prior work on this
topic assumes that we know the probability of each user influencing each other
user, or we have data that lets us estimate these influences. However, this
information is typically not initially available or is difficult to obtain. To
avoid this assumption, we adopt a combinatorial multi-armed bandit paradigm
that estimates the influence probabilities as we sequentially try different
seed sets. We establish bounds on the performance of this procedure under the
existing edge-level feedback as well as a novel and more realistic node-level
feedback. Beyond our theoretical results, we describe a practical
implementation and experimentally demonstrate its efficiency and effectiveness
on four real datasets.Comment: 12 page
An Empirical Evaluation Of Social Influence Metrics
Predicting when an individual will adopt a new behavior is an important
problem in application domains such as marketing and public health. This paper
examines the perfor- mance of a wide variety of social network based
measurements proposed in the literature - which have not been previously
compared directly. We study the probability of an individual becoming
influenced based on measurements derived from neigh- borhood (i.e. number of
influencers, personal network exposure), structural diversity, locality,
temporal measures, cascade mea- sures, and metadata. We also examine the
ability to predict influence based on choice of classifier and how the ratio of
positive to negative samples in both training and testing affect prediction
results - further enabling practical use of these concepts for social influence
applications.Comment: 8 pages, 5 figure
A Data-driven Study of Influences in Twitter Communities
This paper presents a quantitative study of Twitter, one of the most popular
micro-blogging services, from the perspective of user influence. We crawl
several datasets from the most active communities on Twitter and obtain 20.5
million user profiles, along with 420.2 million directed relations and 105
million tweets among the users. User influence scores are obtained from
influence measurement services, Klout and PeerIndex. Our analysis reveals
interesting findings, including non-power-law influence distribution, strong
reciprocity among users in a community, the existence of homophily and
hierarchical relationships in social influences. Most importantly, we observe
that whether a user retweets a message is strongly influenced by the first of
his followees who posted that message. To capture such an effect, we propose
the first influencer (FI) information diffusion model and show through
extensive evaluation that compared to the widely adopted independent cascade
model, the FI model is more stable and more accurate in predicting influence
spreads in Twitter communities.Comment: 11 page
Learning user-specific latent influence and susceptibility from information cascades
Predicting cascade dynamics has important implications for understanding
information propagation and launching viral marketing. Previous works mainly
adopt a pair-wise manner, modeling the propagation probability between pairs of
users using n^2 independent parameters for n users. Consequently, these models
suffer from severe overfitting problem, specially for pairs of users without
direct interactions, limiting their prediction accuracy. Here we propose to
model the cascade dynamics by learning two low-dimensional user-specific
vectors from observed cascades, capturing their influence and susceptibility
respectively. This model requires much less parameters and thus could combat
overfitting problem. Moreover, this model could naturally model
context-dependent factors like cumulative effect in information propagation.
Extensive experiments on synthetic dataset and a large-scale microblogging
dataset demonstrate that this model outperforms the existing pair-wise models
at predicting cascade dynamics, cascade size, and "who will be retweeted".Comment: from The 29th AAAI Conference on Artificial Intelligence (AAAI-2015
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