17 research outputs found
Mean-field Approximation for Stochastic Population Processes in Networks under Imperfect Information
This paper studies a general class of stochastic population processes in
which agents interact with one another over a network. Agents update their
behaviors in a random and decentralized manner based only on their current
state and the states of their neighbors. It is well known that when the number
of agents is large and the network is a complete graph (has all-to-all
information access), the macroscopic behavior of the population converges to a
differential equation called a {\it mean-field approximation}. When the network
is not complete, it is unclear in general whether there exists a suitable
mean-field approximation for the macroscopic behavior of the population. This
paper provides general conditions on the network and policy dynamics for which
a suitable mean-field approximation exists. First, we show that as long as the
network is well-connected, the macroscopic behavior of the population
concentrates around the {\it same} mean-field system as the complete-graph
case. Next, we show that as long as the network is sufficiently dense, the
macroscopic behavior of the population concentrates around a mean-field system
that is, in general, {\it different} from the mean-field system obtained in the
complete-graph case. Finally, we provide conditions under which the mean-field
approximation is equivalent to the one obtained in the complete-graph case.Comment: 59 pages, 4 figure
MARGIN: Uncovering Deep Neural Networks using Graph Signal Analysis
Interpretability has emerged as a crucial aspect of machine learning, aimed
at providing insights into the working of complex neural networks. However,
existing solutions vary vastly based on the nature of the interpretability
task, with each use case requiring substantial time and effort. This paper
introduces MARGIN, a simple yet general approach to address a large set of
interpretability tasks ranging from identifying prototypes to explaining image
predictions. MARGIN exploits ideas rooted in graph signal analysis to determine
influential nodes in a graph, which are defined as those nodes that maximally
describe a function defined on the graph. By carefully defining task-specific
graphs and functions, we demonstrate that MARGIN outperforms existing
approaches in a number of disparate interpretability challenges.Comment: Technical Repor
Recovering the Graph Underlying Networked Dynamical Systems under Partial Observability: A Deep Learning Approach
We study the problem of graph structure identification, i.e., of recovering
the graph of dependencies among time series. We model these time series data as
components of the state of linear stochastic networked dynamical systems. We
assume partial observability, where the state evolution of only a subset of
nodes comprising the network is observed. We devise a new feature vector
computed from the observed time series and prove that these features are
linearly separable, i.e., there exists a hyperplane that separates the cluster
of features associated with connected pairs of nodes from those associated with
disconnected pairs. This renders the features amenable to train a variety of
classifiers to perform causal inference. In particular, we use these features
to train Convolutional Neural Networks (CNNs). The resulting causal inference
mechanism outperforms state-of-the-art counterparts w.r.t. sample-complexity.
The trained CNNs generalize well over structurally distinct networks (dense or
sparse) and noise-level profiles. Remarkably, they also generalize well to
real-world networks while trained over a synthetic network (realization of a
random graph). Finally, the proposed method consistently reconstructs the graph
in a pairwise manner, that is, by deciding if an edge or arrow is present or
absent in each pair of nodes, from the corresponding time series of each pair.
This fits the framework of large-scale systems, where observation or processing
of all nodes in the network is prohibitive.Comment: Accepted at The 37th AAAI Conference on Artificial Intelligence (main
track