Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology
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Optimal Subgraph Structures in Scale-Free Configuration
Submitted to the Annals of Applied Probability OPTIMAL SUBGRAPH STRUCTURES IN SCALE-FREE CONFIGURATION MODELS , By Remco van der Hofstad∗ §, Johan S. H. van , , Leeuwaarden∗ ¶ and Clara Stegehuis∗ k Eindhoven University of Technology§, Tilburg University¶ and Twente Universityk Subgraphs reveal information about the geometry and function- alities of complex networks. For scale-free networks with unbounded degree fluctuations, we obtain the asymptotics of the number of times a small connected graph occurs as a subgraph or as an induced sub- graph. We obtain these results by analyzing the configuration model with degree exponent τ (2, 3) and introducing a novel class of op- ∈ timization problems. For any given subgraph, the unique optimizer describes the degrees of the vertices that together span the subgraph. We find that subgraphs typically occur between vertices with specific degree ranges. In this way, we can count and characterize all sub- graphs. We refrain from double counting in the case of multi-edges, essentially counting the subgraphs in the erased configuration model. 1. Introduction Scale-free networks often have degree distributions that follow power laws with exponent τ (2, 3) [1, 11, 22, 34]. Many net- ∈ works have been reported to satisfy these conditions, including metabolic networks, the internet and social networks. Scale-free networks come with the presence of hubs, i.e., vertices of extremely high degrees. Another property of real-world scale-free networks is that the clustering coefficient (the probability that two uniformly chosen neighbors of a vertex are neighbors themselves) decreases with the vertex degree [4, 10, 24, 32, 34], again following a power law. -
Detecting Statistically Significant Communities
1 Detecting Statistically Significant Communities Zengyou He, Hao Liang, Zheng Chen, Can Zhao, Yan Liu Abstract—Community detection is a key data analysis problem across different fields. During the past decades, numerous algorithms have been proposed to address this issue. However, most work on community detection does not address the issue of statistical significance. Although some research efforts have been made towards mining statistically significant communities, deriving an analytical solution of p-value for one community under the configuration model is still a challenging mission that remains unsolved. The configuration model is a widely used random graph model in community detection, in which the degree of each node is preserved in the generated random networks. To partially fulfill this void, we present a tight upper bound on the p-value of a single community under the configuration model, which can be used for quantifying the statistical significance of each community analytically. Meanwhile, we present a local search method to detect statistically significant communities in an iterative manner. Experimental results demonstrate that our method is comparable with the competing methods on detecting statistically significant communities. Index Terms—Community Detection, Random Graphs, Configuration Model, Statistical Significance. F 1 INTRODUCTION ETWORKS are widely used for modeling the structure function that is able to always achieve the best performance N of complex systems in many fields, such as biology, in all possible scenarios. engineering, and social science. Within the networks, some However, most of these objective functions (metrics) do vertices with similar properties will form communities that not address the issue of statistical significance of commu- have more internal connections and less external links. -
On the Density of a Graph and Its Blowup
On the Density of a Graph and its Blowup Asaf Shapira ¤ Raphael Yustery Abstract It is well-known that, of all graphs with edge-density p, the random graph G(n; p) contains the smallest density of copies of Kt;t, the complete bipartite graph of size 2t. Since Kt;t is a t-blowup of an edge, the following intriguing open question arises: Is it true that of all graphs with triangle 3 density p , the random graph G(n; p) contains close to the smallest density of Kt;t;t, which is the t-blowup of a triangle? Our main result gives an indication that the answer to the above question is positive by showing that for some blowup, the answer must be positive. More formally we prove that if G 3 has triangle density p , then there is some 2 · t · T (p) for which the density of Kt;t;t in G is at (3+o(1))t2 least p , which (up to the o(1) term) equals the density of Kt;t;t in G(n; p). We also raise several open problems related to these problems and discuss some applications to other areas. 1 Introduction One of the main family of problems studied in extremal graph theory is how does the lack and/or number of copies of one graph H in a graph G a®ect the lack and/or number of copies of another graph H0 in G. Perhaps the most well known problems of this type are Ramsey's Theorem and Tur¶an'sTheorem. -
Processes on Complex Networks. Percolation
Chapter 5 Processes on complex networks. Percolation 77 Up till now we discussed the structure of the complex networks. The actual reason to study this structure is to understand how this structure influences the behavior of random processes on networks. I will talk about two such processes. The first one is the percolation process. The second one is the spread of epidemics. There are a lot of open problems in this area, the main of which can be innocently formulated as: How the network topology influences the dynamics of random processes on this network. We are still quite far from a definite answer to this question. 5.1 Percolation 5.1.1 Introduction to percolation Percolation is one of the simplest processes that exhibit the critical phenomena or phase transition. This means that there is a parameter in the system, whose small change yields a large change in the system behavior. To define the percolation process, consider a graph, that has a large connected component. In the classical settings, percolation was actually studied on infinite graphs, whose vertices constitute the set Zd, and edges connect each vertex with nearest neighbors, but we consider general random graphs. We have parameter ϕ, which is the probability that any edge present in the underlying graph is open or closed (an event with probability 1 − ϕ) independently of the other edges. Actually, if we talk about edges being open or closed, this means that we discuss bond percolation. It is also possible to talk about the vertices being open or closed, and this is called site percolation. -
Percolation Thresholds for Robust Network Connectivity
Percolation Thresholds for Robust Network Connectivity Arman Mohseni-Kabir1, Mihir Pant2, Don Towsley3, Saikat Guha4, Ananthram Swami5 1. Physics Department, University of Massachusetts Amherst, [email protected] 2. Massachusetts Institute of Technology 3. College of Information and Computer Sciences, University of Massachusetts Amherst 4. College of Optical Sciences, University of Arizona 5. Army Research Laboratory Abstract Communication networks, power grids, and transportation networks are all examples of networks whose performance depends on reliable connectivity of their underlying network components even in the presence of usual network dynamics due to mobility, node or edge failures, and varying traffic loads. Percolation theory quantifies the threshold value of a local control parameter such as a node occupation (resp., deletion) probability or an edge activation (resp., removal) probability above (resp., below) which there exists a giant connected component (GCC), a connected component comprising of a number of occupied nodes and active edges whose size is proportional to the size of the network itself. Any pair of occupied nodes in the GCC is connected via at least one path comprised of active edges and occupied nodes. The mere existence of the GCC itself does not guarantee that the long-range connectivity would be robust, e.g., to random link or node failures due to network dynamics. In this paper, we explore new percolation thresholds that guarantee not only spanning network connectivity, but also robustness. We define and analyze four measures of robust network connectivity, explore their interrelationships, and numerically evaluate the respective robust percolation thresholds arXiv:2006.14496v1 [physics.soc-ph] 25 Jun 2020 for the 2D square lattice. -
Closest 4-Leaf Power Is Fixed-Parameter Tractable$
Discrete Applied Mathematics 156 (2008) 3345–3361 www.elsevier.com/locate/dam Closest 4-leaf power is fixed-parameter tractableI Michael Dom∗, Jiong Guo, Falk Huffner,¨ Rolf Niedermeier Institut fur¨ Informatik, Friedrich-Schiller-Universitat¨ Jena, Ernst-Abbe-Platz 2, D-07743 Jena, Germany Received 3 August 2006; received in revised form 18 December 2007; accepted 9 January 2008 Available online 4 March 2008 Abstract The NP-complete CLOSEST 4-LEAF POWER problem asks, given an undirected graph, whether it can be modified by at most r edge insertions or deletions such that it becomes a 4-leaf power. Herein, a 4-leaf power is a graph that can be constructed by considering an unrooted tree—the 4-leaf root—with leaves one-to-one labeled by the graph vertices, where we connect two graph vertices by an edge iff their corresponding leaves are at distance at most 4 in the tree. Complementing previous work on CLOSEST 2-LEAF POWER and CLOSEST 3-LEAF POWER, we give the first algorithmic result for CLOSEST 4-LEAF POWER, showing that CLOSEST 4-LEAF POWER is fixed-parameter tractable with respect to the parameter r. c 2008 Elsevier B.V. All rights reserved. Keywords: Fixed-parameter tractability; Graph algorithm; Graph modification; Graph power; Leaf power; Forbidden subgraph characterization 1. Introduction Graph powers form a classical concept in graph theory, and the rich literature dates back to the sixties of the previous century (see [5, Section 10.6] and [27] for surveys). The k-power of an undirected graph G D .V; E/ is the undirected graph Gk D .V; E0/ with .u; v/ 2 E0 iff there is a path of length at most k between u and v in G. -
15 Netsci Configuration Model.Key
Configuring random graph models with fixed degree sequences Daniel B. Larremore Santa Fe Institute June 21, 2017 NetSci [email protected] @danlarremore Brief note on references: This talk does not include references to literature, which are numerous and important. Most (but not all) references are included in the arXiv paper: arxiv.org/abs/1608.00607 Stochastic models, sets, and distributions • a generative model is just a recipe: choose parameters→make the network • a stochastic generative model is also just a recipe: choose parameters→draw a network • since a single stochastic generative model can generate many networks, the model itself corresponds to a set of networks. • and since the generative model itself is some combination or composition of random variables, a random graph model is a set of possible networks, each with an associated probability, i.e., a distribution. this talk: configuration models: uniform distributions over networks w/ fixed deg. seq. Why care about random graphs w/ fixed degree sequence? Since many networks have broad or peculiar degree sequences, these random graph distributions are commonly used for: Hypothesis testing: Can a particular network’s properties be explained by the degree sequence alone? Modeling: How does the degree distribution affect the epidemic threshold for disease transmission? Null model for Modularity, Stochastic Block Model: Compare an empirical graph with (possibly) community structure to the ensemble of random graphs with the same vertex degrees. e a loopy multigraphs c d simple simple loopy graphs multigraphs graphs Stub Matching tono multiedgesdraw from theno config. self-loops model ~k = 1, 2, 2, 1 b { } 3 3 4 3 4 3 4 4 multigraphs 2 5 2 5 2 5 2 5 1 1 6 6 1 6 1 6 3 3 the 4standard algorithm:4 3 4 4 loopy and/or 5 5 draw2 from5 the distribution2 5 by sequential2 “Stub Matching”2 1 1 6 6 1 6 1 6 1. -
A Multigraph Approach to Social Network Analysis
1 Introduction Network data involving relational structure representing interactions between actors are commonly represented by graphs where the actors are referred to as vertices or nodes, and the relations are referred to as edges or ties connecting pairs of actors. Research on social networks is a well established branch of study and many issues concerning social network analysis can be found in Wasserman and Faust (1994), Carrington et al. (2005), Butts (2008), Frank (2009), Kolaczyk (2009), Scott and Carrington (2011), Snijders (2011), and Robins (2013). A common approach to social network analysis is to only consider binary relations, i.e. edges between pairs of vertices are either present or not. These simple graphs only consider one type of relation and exclude the possibility for self relations where a vertex is both the sender and receiver of an edge (also called edge loops or just shortly loops). In contrast, a complex graph is defined according to Wasserman and Faust (1994): If a graph contains loops and/or any pairs of nodes is adjacent via more than one line the graph is complex. [p. 146] In practice, simple graphs can be derived from complex graphs by collapsing the multiple edges into single ones and removing the loops. However, this approach discards information inherent in the original network. In order to use all available network information, we must allow for multiple relations and the possibility for loops. This leads us to the study of multigraphs which has not been treated as extensively as simple graphs in the literature. As an example, consider a network with vertices representing different branches of an organ- isation. -
Critical Window for Connectivity in the Configuration Model 3
CRITICAL WINDOW FOR CONNECTIVITY IN THE CONFIGURATION MODEL LORENZO FEDERICO AND REMCO VAN DER HOFSTAD Abstract. We identify the asymptotic probability of a configuration model CMn(d) to produce a connected graph within its critical window for connectivity that is identified by the number of vertices of degree 1 and 2, as well as the expected degree. In this window, the probability that the graph is connected converges to a non-trivial value, and the size of the complement of the giant component weakly converges to a finite random variable. Under a finite second moment con- dition we also derive the asymptotics of the connectivity probability conditioned on simplicity, from which the asymptotic number of simple connected graphs with a prescribed degree sequence follows. Keywords: configuration model, connectivity threshold, degree sequence MSC 2010: 05A16, 05C40, 60C05 1. Introduction In this paper we consider the configuration model CMn(d) on n vertices with a prescribed degree sequence d = (d1,d2, ..., dn). We investigate the condition on d for CMn(d) to be with high probability connected or disconnected in the limit as n , and we analyse the behaviour of the model in the critical window for connectivity→ ∞ (i.e., when the asymptotic probability of producing a connected graph is in the interval (0, 1)). Given a vertex v [n]= 1, 2, ..., n , we call d its degree. ∈ { } v The configuration model is constructed by assigning dv half-edges to each vertex v, after which the half-edges are paired randomly: first we pick two half-edges at arXiv:1603.03254v1 [math.PR] 10 Mar 2016 random and create an edge out of them, then we pick two half-edges at random from the set of remaining half-edges and pair them into an edge, etc. -
Graph Powers: Hardness Results, Good Characterizations and Efficient Algorithms
Graph Powers: Hardness Results, Good Characterizations and Efficient Algorithms Dissertation zur Erlangung des akademischen Grades Doktor-Ingenieur (Dr.-Ing.) der Fakult¨at f¨ur Informatik und Elektrotechnik der Universit¨at Rostock Vorgelegt von MSc. Ngoc Tuy Nguyen Rostock, November 2009 Gutachter: Prof. Dr. Van Bang Le Universit¨at Rostock. Institut fu¨rInformatik Gutachter: Prof. Dr. Egon Wanke Heinrich- Heine- Universit¨at D¨usseldorf. Institut fu¨rInformatik Gutachter: Prof. Dr. Ekkehard K¨ohler Brandenburgische Technische Universit¨at Cottbus. Institut fu¨rMathematik Tag der ¨offentlichen Verteidigung: 30. Oktober 2009 ii Abstract Given a graph H =(VH , EH ) and a positive integer k, the k-th power of H, written Hk, is the graph obtained from H by adding new edges between any pair of vertices k at distance at most k in H; formally, H =(VH , {xy | 1 ≤ dH (x, y) ≤ k}). A graph G is the k-th power of a graph H if G = Hk, and in this case, H is a k-th root of G. For the cases of k = 2 and k = 3, we say that H2 and H3 is the square, respectively, the cube of H and H is a square root of G = H2, respectively, a cube root of G = H3. In this thesis we study the computational complexity for recognizing k-th pow- ers of general graphs as well as restricted graphs. This work provides new NP- completeness results, good characterizations and efficient algorithms for graph pow- ers. The main results are the following. • There exist reductions proving the NP-completeness for recognizing k-th pow- ers of general graphs for fixed k ≥ 2, recognizing k-th powers of bipartite graphs for fixed k ≥ 3, recognizing k-th powers of chordal graphs, and finding k-th roots of chordal graphs for all fixed k ≥ 2. -
Using Artificial Neural Network to Detect Fetal Alcohol Spectrum
applied sciences Article Using Artificial Neural Network to Detect Fetal Alcohol Spectrum Disorder in Children Vannessa Duarte 1 , Paul Leger 2 , Sergio Contreras 1,* and Hiroaki Fukuda 3 1 Escuela de Ciencias Empresariales, Universidad Católica del Norte, Coquimbo 1780000, Chile; [email protected] 2 Escuela de Ingeniería, Universidad Católica del Norte, Coquimbo 1780000, Chile; [email protected] 3 Shibaura Institute of Technology, Tokio 138-8548, Japan; [email protected] * Correspondence: [email protected] Abstract: Fetal alcohol spectrum disorder (FASD) is an umbrella term for children’s conditions due to their mother having consumed alcohol during pregnancy. These conditions can be mild to severe, affecting the subject’s quality of life. An earlier diagnosis of FASD is crucial for an improved quality of life of children by allowing a better inclusion in the educational system. New trends in computer- based diagnosis to detect FASD include using Machine Learning (ML) tools to detect this syndrome. However, most of these studies rely on children’s images that can be invasive and costly. Therefore, this paper presents a study that focuses on evaluating an ANN to classify children with FASD using non-invasive and more accessible data. This data used comes from a battery of tests obtained from children, including psychometric, saccade eye movement, and diffusion tensor imaging (DTI). We study the different configurations of ANN with dense layers being the psychometric data that correctly perform the best with 75% of the outcome. The other models include a feature layer, and we used it to predict FASD using every test individually. -
Recognition of C4-Free and 1/2-Hyperbolic Graphs David Coudert, Guillaume Ducoffe
Recognition of C4-free and 1/2-hyperbolic graphs David Coudert, Guillaume Ducoffe To cite this version: David Coudert, Guillaume Ducoffe. Recognition of C4-free and 1/2-hyperbolic graphs. SIAM Journal on Discrete Mathematics, Society for Industrial and Applied Mathematics, 2014, 28 (3), pp.1601-1617. 10.1137/140954787. hal-01070768 HAL Id: hal-01070768 https://hal.inria.fr/hal-01070768 Submitted on 2 Oct 2014 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. ∗ Recognition of C4-free and 1/2-hyperbolic graphs David Coudert†‡ Guillaume Ducoffe†‡§ October 2, 2014 Abstract The shortest-path metric d of a connected graph G is 1/2-hyperbolic if, and only if, it satisfies d(u,v) + d(x,y) ≤ max{d(u,x) + d(v,y), d(u,y) + d(v,x)} + 1, for every 4-tuple u,x,v,y of G. We show that the problem of deciding whether an unweighted graph is 1/2-hyperbolic is subcubic equivalent to the problem of determining whether there is a chordless cycle of length 4 in a graph. An improved algorithm is also given for both problems, taking advantage of fast rectangular matrix multiplication.