Chip-firing may be much faster than you think

A new bound (Theorem \ref{thm:main}) for the duration of the chip-firing game with $N$ chips on a $n$-vertex graph is obtained, by a careful analysis of the pseudo-inverse of the discrete Laplacian matrix of the graph. This new bound is expressed in terms of the entries of the pseudo-inverse. It is shown (Section 5) to be always better than the classic bound due to Bj{\"o}rner, Lov\'{a}sz and Shor. In some cases the improvement is dramatic. For instance: for strongly regular graphs the classic and the new bounds reduce to $O(nN)$ and $O(n+N)$, respectively. For dense regular graphs - $d=(\frac{1}{2}+\epsilon)n$ - the classic and the new bounds reduce to $O(N)$ and $O(n)$, respectively. This is a snapshot of a work in progress, so further results in this vein are in the works

Cascading Failures in Power Grids - Analysis and Algorithms

This paper focuses on cascading line failures in the transmission system of the power grid. Recent large-scale power outages demonstrated the limitations of percolation- and epid- emic-based tools in modeling cascades. Hence, we study cascades by using computational tools and a linearized power flow model. We first obtain results regarding the Moore-Penrose pseudo-inverse of the power grid admittance matrix. Based on these results, we study the impact of a single line failure on the flows on other lines. We also illustrate via simulation the impact of the distance and resistance distance on the flow increase following a failure, and discuss the difference from the epidemic models. We then study the cascade properties, considering metrics such as the distance between failures and the fraction of demand (load) satisfied after the cascade (yield). We use the pseudo-inverse of admittance matrix to develop an efficient algorithm to identify the cascading failure evolution, which can be a building block for cascade mitigation. Finally, we show that finding the set of lines whose removal has the most significant impact (under various metrics) is NP-Hard and introduce a simple heuristic for the minimum yield problem. Overall, the results demonstrate that using the resistance distance and the pseudo-inverse of admittance matrix provides important insights and can support the development of efficient algorithms

M-Matrix Inverse problem for distance-regular graphs

We analyze when the Moore–Penrose inverse of the combinatorial Laplacian of a distance–regular graph is a M–matrix;that is, it has non–positive off–diagonal elements or, equivalently when the Moore-Penrose inverse of the combinatorial Laplacian of a distance–regular graph is also the combinatorial Laplacian of another network. When this occurs we say that the distance–regular graph has the M–property. We prove that only distance–regular graphs with diameter up to three can have the M–property and we give a characterization of the graphs that satisfy the M-property in terms of their intersection array. Moreover we exhaustively analyze the strongly regular graphs having the M-property and we give some families of distance regular graphs with diameter three that satisfy the M-property.Peer Reviewe

M-Matrix Inverse problem for distance-regular graphs

Postprint (published version

Mean first passage time for distance-biregular graphs

Postprint (published version

Sketch-based Randomized Algorithms for Dynamic Graph Regression

A well-known problem in data science and machine learning is {\em linear regression}, which is recently extended to dynamic graphs. Existing exact algorithms for updating the solution of dynamic graph regression problem require at least a linear time (in terms of $n$: the size of the graph). However, this time complexity might be intractable in practice. In the current paper, we utilize {\em subsampled randomized Hadamard transform} and \textsf{CountSketch} to propose the first randomized algorithms. Suppose that we are given an $n\times m$ matrix embedding $M$ of the graph, where $m \ll n$. Let $r$ be the number of samples required for a guaranteed approximation error, which is a sublinear function of $n$. Our first algorithm reduces time complexity of pre-processing to $O(n(m + 1) + 2n(m + 1) \log_2(r + 1) + rm^2)$. Then after an edge insertion or an edge deletion, it updates the approximate solution in $O(rm)$ time. Our second algorithm reduces time complexity of pre-processing to $O \left( nnz(M) + m^3 \epsilon^{-2} \log^7(m/\epsilon) \right)$, where $nnz(M)$ is the number of nonzero elements of $M$. Then after an edge insertion or an edge deletion or a node insertion or a node deletion, it updates the approximate solution in $O(qm)$ time, with $q=O\left(\frac{m^2}{\epsilon^2} \log^6(m/\epsilon) \right)$. Finally, we show that under some assumptions, if $\ln n < \epsilon^{-1}$ our first algorithm outperforms our second algorithm and if $\ln n \geq \epsilon^{-1}$ our second algorithm outperforms our first algorithm