941 research outputs found
Community detection in directed acyclic graphs
Some temporal networks, most notably citation networks, are naturally
represented as directed acyclic graphs (DAGs). To detect communities in DAGs,
we propose a modularity for DAGs by defining an appropriate null model (i.e.,
randomized network) respecting the order of nodes. We implement a spectral
method to approximately maximize the proposed modularity measure and test the
method on citation networks and other DAGs. We find that the attained values of
the modularity for DAGs are similar for partitions that we obtain by maximizing
the proposed modularity (designed for DAGs), the modularity for undirected
networks and that for general directed networks. In other words, if we neglect
the order imposed on nodes (and the direction of links) in a given DAG and
maximize the conventional modularity measure, the obtained partition is close
to the optimal one in the sense of the modularity for DAGs.Comment: 2 figures, 7 table
Reduced-Dimension Linear Transform Coding of Correlated Signals in Networks
A model, called the linear transform network (LTN), is proposed to analyze
the compression and estimation of correlated signals transmitted over directed
acyclic graphs (DAGs). An LTN is a DAG network with multiple source and
receiver nodes. Source nodes transmit subspace projections of random correlated
signals by applying reduced-dimension linear transforms. The subspace
projections are linearly processed by multiple relays and routed to intended
receivers. Each receiver applies a linear estimator to approximate a subset of
the sources with minimum mean squared error (MSE) distortion. The model is
extended to include noisy networks with power constraints on transmitters. A
key task is to compute all local compression matrices and linear estimators in
the network to minimize end-to-end distortion. The non-convex problem is solved
iteratively within an optimization framework using constrained quadratic
programs (QPs). The proposed algorithm recovers as special cases the regular
and distributed Karhunen-Loeve transforms (KLTs). Cut-set lower bounds on the
distortion region of multi-source, multi-receiver networks are given for linear
coding based on convex relaxations. Cut-set lower bounds are also given for any
coding strategy based on information theory. The distortion region and
compression-estimation tradeoffs are illustrated for different communication
demands (e.g. multiple unicast), and graph structures.Comment: 33 pages, 7 figures, To appear in IEEE Transactions on Signal
Processin
Making communities show respect for order
In this work we give a community detection algorithm in which the communities both respects the intrinsic order of a directed acyclic graph and also finds similar nodes. We take inspiration from classic similarity measures of bibliometrics, used to assess how similar two publications are, based on their relative citation patterns. We study the algorithm’s performance and antichain properties in artificial models and in real networks, such as citation graphs and food webs. We show how well this partitioning algorithm distinguishes and groups together nodes of the same origin (in a citation network, the origin is a topic or a research field). We make the comparison between our partitioning algorithm and standard hierarchical layering tools as well as community detection methods. We show that our algorithm produces different communities from standard layering algorithms
4Ward: a Relayering Strategy for Efficient Training of Arbitrarily Complex Directed Acyclic Graphs
Thanks to their ease of implementation, multilayer perceptrons (MLPs) have
become ubiquitous in deep learning applications. The graph underlying an MLP is
indeed multipartite, i.e. each layer of neurons only connects to neurons
belonging to the adjacent layer. In contrast, in vivo brain connectomes at the
level of individual synapses suggest that biological neuronal networks are
characterized by scale-free degree distributions or exponentially truncated
power law strength distributions, hinting at potentially novel avenues for the
exploitation of evolution-derived neuronal networks. In this paper, we present
``4Ward'', a method and Python library capable of generating flexible and
efficient neural networks (NNs) from arbitrarily complex directed acyclic
graphs. 4Ward is inspired by layering algorithms drawn from the graph drawing
discipline to implement efficient forward passes, and provides significant time
gains in computational experiments with various Erd\H{o}s-R\'enyi graphs. 4Ward
not only overcomes the sequential nature of the learning matrix method, by
parallelizing the computation of activations, but also addresses the
scalability issues encountered in the current state-of-the-art and provides the
designer with freedom to customize weight initialization and activation
functions. Our algorithm can be of aid for any investigator seeking to exploit
complex topologies in a NN design framework at the microscale
Formal vs self-organised knowledge systems: a network approach
In this work we consider the topological analysis of symbolic formal systems
in the framework of network theory. In particular we analyse the network
extracted by Principia Mathematica of B. Russell and A.N. Whitehead, where the
vertices are the statements and two statements are connected with a directed
link if one statement is used to demonstrate the other one. We compare the
obtained network with other directed acyclic graphs, such as a scientific
citation network and a stochastic model. We also introduce a novel topological
ordering for directed acyclic graphs and we discuss its properties in respect
to the classical one. The main result is the observation that formal systems of
knowledge topologically behave similarly to self-organised systems.Comment: research pape
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