16 research outputs found
Better Bounds on the Adaptivity Gap of Influence Maximization under Full-adoption Feedback
In the influence maximization (IM) problem, we are given a social network and
a budget , and we look for a set of nodes in the network, called seeds,
that maximize the expected number of nodes that are reached by an influence
cascade generated by the seeds, according to some stochastic model for
influence diffusion. In this paper, we study the adaptive IM, where the nodes
are selected sequentially one by one, and the decision on the th seed can be
based on the observed cascade produced by the first seeds. We focus on
the full-adoption feedback in which we can observe the entire cascade of each
previously selected seed and on the independent cascade model where each edge
is associated with an independent probability of diffusing influence.
Our main result is the first sub-linear upper bound that holds for any graph.
Specifically, we show that the adaptivity gap is upper-bounded by , where is the number of nodes in the graph. Moreover, we
improve over the known upper bound for in-arborescences from
to . Finally, we
study -bounded graphs, a class of undirected graphs in which the sum of
node degrees higher than two is at most , and show that the adaptivity
gap is upper-bounded by . Moreover, we show that in
0-bounded graphs, i.e. undirected graphs in which each connected component is a
path or a cycle, the adaptivity gap is at most . To prove our bounds, we introduce new techniques to relate adaptive
policies with non-adaptive ones that might be of their own interest.Comment: 18 page
Gaps in Information Access in Social Networks
The study of influence maximization in social networks has largely ignored
disparate effects these algorithms might have on the individuals contained in
the social network. Individuals may place a high value on receiving
information, e.g. job openings or advertisements for loans. While
well-connected individuals at the center of the network are likely to receive
the information that is being distributed through the network, poorly connected
individuals are systematically less likely to receive the information,
producing a gap in access to the information between individuals. In this work,
we study how best to spread information in a social network while minimizing
this access gap. We propose to use the maximin social welfare function as an
objective function, where we maximize the minimum probability of receiving the
information under an intervention. We prove that in this setting this welfare
function constrains the access gap whereas maximizing the expected number of
nodes reached does not. We also investigate the difficulties of using the
maximin, and present hardness results and analysis for standard greedy
strategies. Finally, we investigate practical ways of optimizing for the
maximin, and give empirical evidence that a simple greedy-based strategy works
well in practice.Comment: Accepted at The Web Conference 201
Mining Implicit Relevance Feedback from User Behavior for Web Question Answering
Training and refreshing a web-scale Question Answering (QA) system for a
multi-lingual commercial search engine often requires a huge amount of training
examples. One principled idea is to mine implicit relevance feedback from user
behavior recorded in search engine logs. All previous works on mining implicit
relevance feedback target at relevance of web documents rather than passages.
Due to several unique characteristics of QA tasks, the existing user behavior
models for web documents cannot be applied to infer passage relevance. In this
paper, we make the first study to explore the correlation between user behavior
and passage relevance, and propose a novel approach for mining training data
for Web QA. We conduct extensive experiments on four test datasets and the
results show our approach significantly improves the accuracy of passage
ranking without extra human labeled data. In practice, this work has proved
effective to substantially reduce the human labeling cost for the QA service in
a global commercial search engine, especially for languages with low resources.
Our techniques have been deployed in multi-language services.Comment: Accepted by KDD 202