Spectral radius of the Harary matrix of the join product of regular graphs1

The distance between two vertices is equal to the number of edges on the shortest path connecting them. The Harary matrix of a simple, undirected, connected and unweighted graph of n vertices is an nonnegative matrix of order n, such that the (i, j)-entry is equal to the reciprocal distance between the vertices vi and Vj if the vertices are different and zero if are equal. In this work we found bounds for the spectral radius of the Harary matrix of the join product of regular graphs.

Better Distance Preservers and Additive Spanners

We study two popular ways to sketch the shortest path distances of an input graph. The first is distance preservers , which are sparse subgraphs that agree with the distances of the original graph on a given set of demand pairs. Prior work on distance preservers has exploited only a simple structural property of shortest paths, called consistency , stating that one can break shortest path ties such that no two paths intersect, split apart, and then intersect again later. We prove that consistency alone is not enough to understand distance preservers, by showing both a lower bound on the power of consistency and a new general upper bound that polynomially surpasses it. Specifically, our new upper bound is that any p demand pairs in an n -node undirected unweighted graph have a distance preserver on O( n 2/3 p 2/3 + np 1/3 edges. We leave a conjecture that the right bound is O ( n 2/3 p 2/3 + n ) or better. The second part of this paper leverages these distance preservers in a new construction of additive spanners , which are subgraphs that preserve all pairwise distances up to an additive error function. We give improved error bounds for spanners with relatively few edges; for example, we prove that all graphs have spanners on O(n) edges with + O ( n 3/7 + ε ) error. Our construction can be viewed as an extension of the popular path-buying framework to clusters of larger radii.

Almost Tight Lower Bounds for Hard Cutting Problems in Embedded Graphs

We prove essentially tight lower bounds, conditionally to the Exponential Time Hypothesis, for two fundamental but seemingly very different cutting problems on surface-embedded graphs: the Shortest Cut Graph problem and the Multiway Cut problem. A cut graph of a graph G embedded on a surface S is a subgraph of G whose removal from S leaves a disk. We consider the problem of deciding whether an unweighted graph embedded on a surface of genus G has a cut graph of length at most a given value. We prove a time lower bound for this problem of n Ω( g log g ) conditionally to the ETH. In other words, the first n O(g) -time algorithm by Erickson and Har-Peled [SoCG 2002, Discr. Comput. Geom. 2004] is essentially optimal. We also prove that the problem is W[1]-hard when parameterized by the genus, answering a 17-year-old question of these authors. A multiway cut of an undirected graph G with t distinguished vertices, called terminals , is a set of edges whose removal disconnects all pairs of terminals. We consider the problem of deciding whether an unweighted graph G has a multiway cut of weight at most a given value. We prove a time lower bound for this problem of n Ω( gt + g 2 + t log ( g + t )) , conditionally to the ETH, for any choice of the genus g ≥ 0 of the graph and the number of terminals t ≥ 4. In other words, the algorithm by the second author [Algorithmica 2017] (for the more general multicut problem) is essentially optimal; this extends the lower bound by the third author [ICALP 2012] (for the planar case). Reductions to planar problems usually involve a gridlike structure. The main novel idea for our results is to understand what structures instead of grids are needed if we want to exploit optimally a certain value G of the genus.

Utility-Aware Graph Dimensionality Reduction Approach

In recent years graphs with massive nodes and edges have become widely used in various application fields, for example, social networks, web mining, traffic on transport, and more. Several researchers have shown that reducing the dimensions is very important in analyzing extensive graph data. They applied a variety of dimensionality reduction strategies, including linear methods or nonlinear methods. However, it is still not clear to what extent the information is lost or preserved when these techniques are applied to reduce the dimensions of large networks. In this study, we measured the utility of graph dimensionality reduction, and we proved when using the very recently suggested method, which is HDR to reduce dimensional for graph, the utility loss will be small compared with popular linear techniques, such as PCA, LDA, FA, and MDS. We measured the utility based on three essential network metrics: Average Clustering Coefficient (ACC), Average Path Length (APL), and Average Betweenness (ABW). The results showed that HDR achieved a lower rate of utility loss compared to other dimensionality reduction methods. We performed our experiments on the three undirected and unweighted graph datasets.

Complexity of inheritance of ℱ-convexity for restricted games induced by minimum partitions

Let G = (N, E, w) be a weighted communication graph. For any subset A ⊆ N, we delete all minimum-weight edges in the subgraph induced by A. The connected components of the resultant subgraph constitute the partition 𝒫min(A) of A. Then, for every cooperative game (N, v), the 𝒫min-restricted game (N, v̅) is defined by v̅(A)=∑F∈𝒫min(A)v(F) for all A ⊆ N. We prove that we can decide in polynomial time if there is inheritance of ℱ-convexity, i.e., if for every ℱ-convex game the 𝒫min-restricted game is ℱ-convex, where ℱ-convexity is obtained by restricting convexity to connected subsets. This implies that we can also decide in polynomial time for any unweighted graph if there is inheritance of convexity for Myerson’s graph-restricted game.

An SIR epidemic on a weighted network

We introduce a weighted configuration model graph, where edge weights correspond to the probability of infection in an epidemic on the graph. On these graphs, we study the development of a Susceptible–Infectious–Recovered epidemic using both Reed–Frost and Markovian settings. For the special case of having two different edge types, we determine the basic reproduction numberR0, the probability of a major outbreak, and the relative final size of a major outbreak. Results are compared with those for a calibrated unweighted graph. The degree distributions are based on both theoretical constructs and empirical network data. In addition, bivariate standard normal copulas are used to model the dependence between the degrees of the two edge types, allowing for modeling the correlation between edge types over a wide range. Among the results are that the weighted graph produces much richer results than the unweighted graph. Also, while R0 always increases with increasing correlation between the two degrees, this is not necessarily true for the probability of a major outbreak nor for the relative final size of a major outbreak. When using copulas we see that these can produce results that are similar to those of the empirical degree distributions, indicating that in some cases a copula is a viable alternative to using the full empirical data.

Modifikasi Algoritme Bellman-Ford Untuk Pencarian Rute Terpendek Berdasarkan Kondisi Jalan

The application of the Bellman-ford algorithm for finding the shortest path both weighted and unweighted graph has a weakness in determining the shortest path based on road conditions. This study modified the Bellman-Ford algorithm by adding the Technique for Order of Preference by Similarity to the Ideal Solution method to provide alternative road assessments based on its condition criteria including road density, road width, travel time, and distance. This modified Bellman-Ford has better performance in finding the alternative shortest path by choosing a road with smoother conditions, even though distance and travel time increase.