14 research outputs found
Online Influence Maximization under Independent Cascade Model with Semi-Bandit Feedback
We study the online influence maximization problem in social networks under
the independent cascade model. Specifically, we aim to learn the set of "best
influencers" in a social network online while repeatedly interacting with it.
We address the challenges of (i) combinatorial action space, since the number
of feasible influencer sets grows exponentially with the maximum number of
influencers, and (ii) limited feedback, since only the influenced portion of
the network is observed. Under a stochastic semi-bandit feedback, we propose
and analyze IMLinUCB, a computationally efficient UCB-based algorithm. Our
bounds on the cumulative regret are polynomial in all quantities of interest,
achieve near-optimal dependence on the number of interactions and reflect the
topology of the network and the activation probabilities of its edges, thereby
giving insights on the problem complexity. To the best of our knowledge, these
are the first such results. Our experiments show that in several representative
graph topologies, the regret of IMLinUCB scales as suggested by our upper
bounds. IMLinUCB permits linear generalization and thus is both statistically
and computationally suitable for large-scale problems. Our experiments also
show that IMLinUCB with linear generalization can lead to low regret in
real-world online influence maximization.Comment: Compared with the previous version, this version has fixed a mistake.
This version is also consistent with the NIPS camera-ready versio
Cascade Size Distributions: Why They Matter and How to Compute Them Efficiently
Cascade models are central to understanding, predicting, and controlling
epidemic spreading and information propagation. Related optimization, including
influence maximization, model parameter inference, or the development of
vaccination strategies, relies heavily on sampling from a model. This is either
inefficient or inaccurate. As alternative, we present an efficient message
passing algorithm that computes the probability distribution of the cascade
size for the Independent Cascade Model on weighted directed networks and
generalizations. Our approach is exact on trees but can be applied to any
network topology. It approximates locally tree-like networks well, scales to
large networks, and can lead to surprisingly good performance on more dense
networks, as we also exemplify on real world data.Comment: Accepted at AAAI 202
On the Approximation Relationship between Optimizing Ratio of Submodular (RS) and Difference of Submodular (DS) Functions
We demonstrate that from an algorithm guaranteeing an approximation factor
for the ratio of submodular (RS) optimization problem, we can build another
algorithm having a different kind of approximation guarantee -- weaker than the
classical one -- for the difference of submodular (DS) optimization problem,
and vice versa. We also illustrate the link between these two problems by
analyzing a \textsc{Greedy} algorithm which approximately maximizes objective
functions of the form , where are two non-negative, monotone,
submodular functions and is a {quasiconvex} 2-variables function, which
is non decreasing with respect to the first variable. For the choice
, we recover RS, and for the choice
, we recover DS. To the best of our knowledge, this
greedy approach is new for DS optimization. For RS optimization, it reduces to
the standard \textsc{GreedRatio} algorithm that has already been analyzed
previously. However, our analysis is novel for this case
Contextual Centrality: Going Beyond Network Structures
Centrality is a fundamental network property which ranks nodes by their
structural importance. However, structural importance may not suffice to
predict successful diffusions in a wide range of applications, such as
word-of-mouth marketing and political campaigns. In particular, nodes with high
structural importance may contribute negatively to the objective of the
diffusion. To address this problem, we propose contextual centrality, which
integrates structural positions, the diffusion process, and, most importantly,
nodal contributions to the objective of the diffusion. We perform an empirical
analysis of the adoption of microfinance in Indian villages and weather
insurance in Chinese villages. Results show that contextual centrality of the
first-informed individuals has higher predictive power towards the eventual
adoption outcomes than other standard centrality measures. Interestingly, when
the product of diffusion rate and the largest eigenvalue is
larger than one and diffusion period is long, contextual centrality linearly
scales with eigenvector centrality. This approximation reveals that contextual
centrality identifies scenarios where a higher diffusion rate of individuals
may negatively influence the cascade payoff. Further simulations on the
synthetic and real-world networks show that contextual centrality has the
advantage of selecting an individual whose local neighborhood generates a high
cascade payoff when . Under this condition, stronger homophily
leads to higher cascade payoff. Our results suggest that contextual centrality
captures more complicated dynamics on networks and has significant implications
for applications, such as information diffusion, viral marketing, and political
campaigns
Clinical trial of an AI-augmented intervention for HIV prevention in youth experiencing homelessness
Youth experiencing homelessness (YEH) are subject to substantially greater
risk of HIV infection, compounded both by their lack of access to stable
housing and the disproportionate representation of youth of marginalized
racial, ethnic, and gender identity groups among YEH. A key goal for health
equity is to improve adoption of protective behaviors in this population. One
promising strategy for intervention is to recruit peer leaders from the
population of YEH to promote behaviors such as condom usage and regular HIV
testing to their social contacts. This raises a computational question: which
youth should be selected as peer leaders to maximize the overall impact of the
intervention? We developed an artificial intelligence system to optimize such
social network interventions in a community health setting. We conducted a
clinical trial enrolling 713 YEH at drop-in centers in a large US city. The
clinical trial compared interventions planned with the algorithm to those where
the highest-degree nodes in the youths' social network were recruited as peer
leaders (the standard method in public health) and to an observation-only
control group. Results from the clinical trial show that youth in the AI group
experience statistically significant reductions in key risk behaviors for HIV
transmission, while those in the other groups do not. This provides, to our
knowledge, the first empirical validation of the usage of AI methods to
optimize social network interventions for health. We conclude by discussing
lessons learned over the course of the project which may inform future attempts
to use AI in community-level interventions