Can Negligible Cooperation Increase Network Capacity? The Average-Error Case

In communication networks, cooperative strategies are coding schemes where network nodes work together to improve network performance metrics such as sum-rate. This work studies encoder cooperation in the setting of a discrete multiple access channel with two encoders and a single decoder. A node in the network that is connected to both encoders via rate-limited links, referred to as the cooperation facilitator (CF), enables the cooperation strategy. Previously, the authors presented a class of multiple access channels where the average-error sum-capacity has an infinite derivative in the limit where CF output link capacities approach zero. The authors also demonstrated that for some channels, the maximal-error sum-capacity is not continuous at the point where the output link capacities of the CF equal zero. This work shows that the the average-error sum-capacity is continuous when CF output link capacities converge to zero; that is, the infinite derivative of the average-error sum-capacity is not a result of its discontinuity as in the maximal-error case.Comment: 20 pages, 1 figure. To be submitted to ISIT '1

Negligible Cooperation: Contrasting the Maximal- and Average-Error Cases

In communication networks, cooperative strategies are coding schemes where network nodes work together to improve network performance metrics such as the total rate delivered across the network. This work studies encoder cooperation in the setting of a discrete multiple access channel (MAC) with two encoders and a single decoder. A network node, here called the cooperation facilitator (CF), that is connected to both encoders via rate-limited links, enables the cooperation strategy. Previous work by the authors presents two classes of MACs: (i) one class where the average-error sum-capacity has an infinite derivative in the limit where CF output link capacities approach zero, and (ii) a second class of MACs where the maximal-error sum-capacity is not continuous at the point where the output link capacities of the CF equal zero. This work contrasts the power of the CF in the maximal- and average-error cases, showing that a constant number of bits communicated over the CF output link can yield a positive gain in the maximal-error sum-capacity, while a far greater number of bits, even numbers that grow sublinearly in the blocklength, can never yield a non-negligible gain in the average-error sum-capacity

Converse to the Parter–Wiener theorem: The case of non-trees

AbstractThrough a succession of results, it is known that if the graph of an Hermitian matrix A is a tree and if for some index j, λ∈σ(A)∩σ(A(j)), then there is an index i such that the multiplicity of λ in σ(A(i)) is one more than that in A. We exhibit a converse to this result by showing that it is generally true only for trees. In particular, it is shown that the minimum rank of a positive semidefinite matrix with a given graph G is ⩽n-2 when G is not a tree. This raises the question of how the minimum rank of a positive semidefinite matrix depends upon the graph in general

Efficient Structures for Innovative Social Networks

What lines of communication among members of an organization are most effective in the early, ideation phase of innovation? We investigate this question with a recombination and selection model of knowledge transfer operating through a social network. We measure cost in human time, and seek efficient social network structures in the time--total cost plane (minimize ideation time subject to an upper bound on total cost, or vice versa) and in the time--cost per period plane, with a similar interpretation. Our results suggest that efficiently innovative organizations look nothing like what one intuitively associates with standard formal organizations with strict and unchanging lines of communication, nor do they conform with what one might expect from static social network representations of communication patterns. Rather, ideation is accelerated when people dynamically churn through a large (ideally the entire population) set of conversational partners over time, which naturally begets short path lengths and eliminates information bottlenecks. In organizations with these features group meetings do not help and can hurt the process, because many parallel conversations can achieve the same or better results as one-to-many communications. A family of networks called the complete wheel-stars emerges as an important family on the time-cost efficient frontier. Wheel-star graphs have a completely connected clique of agents at the center, with all other agents connected to the core but not to each other; the star and the complete graph are its extreme elements. We discuss the consequences of these results for organizations and sociometric analyses.http://deepblue.lib.umich.edu/bitstream/2027.42/64992/1/1136_lovejoy.pd

Toughness of Recursively Partitionable Graphs

A simple graph G = (V,E) on n vertices is said to be recursively partitionable (RP) if G ≃ K1, or if G is connected and satisfies the following recursive property: for every integer partition a1, a2, . . . , ak of n, there is a partition {A1,A2, . . . ,Ak} of V such that each |Ai| = ai, and each induced subgraph G[Ai] is RP (1 ≤ i ≤ k). We show that if S is a vertex cut of an RP graph G with |S| ≥ 2, then G−S has at most 3|S| − 1 components. Moreover, this bound is sharp for |S| = 3. We present two methods for constructing new RP graphs from old. We use these methods to show that for all positive integers s, there exist infinitely many RP graphs with an s-vertex cut whose removal leaves 2s + 1 components. Additionally, we prove a simple necessary condition for a graph to have an RP spanning tree, and we characterise a class of minimal 2-connected RP graphs

High-Dimensional Gaussian Graphical Model Selection: Walk Summability and Local Separation Criterion

We consider the problem of high-dimensional Gaussian graphical model selection. We identify a set of graphs for which an efficient estimation algorithm exists, and this algorithm is based on thresholding of empirical conditional covariances. Under a set of transparent conditions, we establish structural consistency (or sparsistency) for the proposed algorithm, when the number of samples n=omega(J_{min}^{-2} log p), where p is the number of variables and J_{min} is the minimum (absolute) edge potential of the graphical model. The sufficient conditions for sparsistency are based on the notion of walk-summability of the model and the presence of sparse local vertex separators in the underlying graph. We also derive novel non-asymptotic necessary conditions on the number of samples required for sparsistency