454 research outputs found
Gradient Method for Continuous Influence Maximization with Budget-Saving Considerations
Continuous influence maximization (CIM) generalizes the original influence
maximization by incorporating general marketing strategies: a marketing
strategy mix is a vector such that for each
node in a social network, could be activated as a seed of diffusion
with probability , where is a strategy activation
function satisfying DR-submodularity. CIM is the task of selecting a strategy
mix with constraint where is a budget
constraint, such that the total number of activated nodes after the diffusion
process, called influence spread and denoted as , is
maximized. In this paper, we extend CIM to consider budget saving, that is,
each strategy mix has a cost where is a
convex cost function, we want to maximize the balanced sum where is a balance parameter, subject
to the constraint of . We denote this problem as
CIM-BS. The objective function of CIM-BS is neither monotone, nor DR-submodular
or concave, and thus neither the greedy algorithm nor the standard result on
gradient method could be directly applied. Our key innovation is the
combination of the gradient method with reverse influence sampling to design
algorithms that solve CIM-BS: For the general case, we give an algorithm that
achieves -approximation, and for the case
of independent strategy activations, we present an algorithm that achieves
approximation.Comment: To appear in AAAI-20, 43 page
Learning Graph Convolutional Network for Skeleton-based Human Action Recognition by Neural Searching
Human action recognition from skeleton data, fueled by the Graph
Convolutional Network (GCN), has attracted lots of attention, due to its
powerful capability of modeling non-Euclidean structure data. However, many
existing GCN methods provide a pre-defined graph and fix it through the entire
network, which can loss implicit joint correlations. Besides, the mainstream
spectral GCN is approximated by one-order hop, thus higher-order connections
are not well involved. Therefore, huge efforts are required to explore a better
GCN architecture. To address these problems, we turn to Neural Architecture
Search (NAS) and propose the first automatically designed GCN for
skeleton-based action recognition. Specifically, we enrich the search space by
providing multiple dynamic graph modules after fully exploring the
spatial-temporal correlations between nodes. Besides, we introduce multiple-hop
modules and expect to break the limitation of representational capacity caused
by one-order approximation. Moreover, a sampling- and memory-efficient
evolution strategy is proposed to search an optimal architecture for this task.
The resulted architecture proves the effectiveness of the higher-order
approximation and the dynamic graph modeling mechanism with temporal
interactions, which is barely discussed before. To evaluate the performance of
the searched model, we conduct extensive experiments on two very large scaled
datasets and the results show that our model gets the state-of-the-art results.Comment: Accepted by AAAI202
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