The Intersection of a Matroid and a Simplicial Complex 1
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Spectral Gap Bounds for the Simplicial Laplacian and an Application To
Spectral gap bounds for the simplicial Laplacian and an application to random complexes Samir Shukla,∗ D. Yogeshwaran† Abstract In this article, we derive two spectral gap bounds for the reduced Laplacian of a general simplicial complex. Our two bounds are proven by comparing a simplicial complex in two different ways with a larger complex and with the corresponding clique complex respectively. Both of these bounds generalize the result of Aharoni et al. (2005) [1] which is valid only for clique complexes. As an application, we investigate the thresholds for vanishing of cohomology of the neighborhood complex of the Erdös- Rényi random graph. We improve the upper bound derived in Kahle (2007) [15] by a logarithmic factor using our spectral gap bounds and we also improve the lower bound via finer probabilistic estimates than those in Kahle (2007) [15]. Keywords: spectral bounds, Laplacian, cohomology, neighborhood complex, Erdös-Rényi random graphs. AMS MSC 2010: 05C80. 05C50. 05E45. 55U10. 1 Introduction Let G be a graph with vertex set V (G) (often to be abbreviated as V ) and let L(G) denote the (unnormalized) Laplacian of G. Let 0 = λ1(G) ≤ λ2(G) ≤ . ≤ λ|V |(G) denote the eigenvalues of L(G) in ascending order. Here, the second smallest eigenvalue λ2(G) is called the spectral gap. The clique complex of a graph G is the simplicial complex whose simplices are all subsets σ ⊂ V which spans a complete subgraph of G. We shall denote the kth k arXiv:1810.10934v2 [math.CO] 9 Oct 2019 reduced cohomology of a simplicial complex X by H (X). -
Message Passing Simplicial Networks
Weisfeiler and Lehman Go Topological: Message Passing Simplicial Networks Cristian Bodnar * 1 Fabrizio Frasca * 2 3 Yu Guang Wang * 4 5 6 Nina Otter 7 Guido Montufar´ * 4 7 Pietro Lio` 1 Michael M. Bronstein 2 3 v0 Abstract v6 v10 v9 The pairwise interaction paradigm of graph ma- v chine learning has predominantly governed the 8 v v modelling of relational systems. However, graphs 1 5 v3 alone cannot capture the multi-level interactions v v present in many complex systems and the expres- 4 7 v2 sive power of such schemes was proven to be lim- ited. To overcome these limitations, we propose Figure 1. Message Passing with upper and boundary adjacencies Message Passing Simplicial Networks (MPSNs), illustrated for vertex v2 and edge (v5; v7) in the complex. a class of models that perform message passing on simplicial complexes (SCs). To theoretically anal- 1. Introduction yse the expressivity of our model we introduce a Simplicial Weisfeiler-Lehman (SWL) colouring Graphs are among the most common abstractions for com- procedure for distinguishing non-isomorphic SCs. plex systems of relations and interactions, arising in a broad We relate the power of SWL to the problem of range of fields from social science to high energy physics. distinguishing non-isomorphic graphs and show Graph neural networks (GNNs), which trace their origins that SWL and MPSNs are strictly more power- to the 1990s (Sperduti, 1994; Goller & Kuchler, 1996; Gori ful than the WL test and not less powerful than et al., 2005; Scarselli et al., 2009; Bruna et al., 2014; Li et al., the 3-WL test. -
Some Properties of Glued Graphs at Complete Clone
Sci.Int.(Lahore),27(1),39-47,2014 ISSN 1013-5316; CODEN: SINTE 8 39 SOME PROPERTIES OF GLUED GRAPHS AT COMPLETE CLONE IN THE VIEW OF ALGEBRAIC COMBINATORICS Seyyede Masoome Seyyedi , Farhad Rahmati Faculty of Mathematics and Computer Science, Amirkabir University of Technology, Tehran, Iran. Corresponding Author: Email: [email protected] ABSTRACT: A glued graph at complete clone is obtained from combining two graphs by identifying edges of of each original graph. We investigate how to change some properties such as height, big height, Krull dimension, Betti numbers by gluing of two graphs at complete clone. We give a sufficient and necessary condition so that the glued graph of two Cohen-Macaulay chordal graphs at complete clone is a Cohen-Macaulay graph. Moreover, we present the conditions that the edge ideal of gluing of two graphs at complete clone has linear resolution whenever the edge ideals of original graphs have linear resolution. We show when gluing of two independence complexes, line graphs, complement graphs can be expressed as independence complex, line graph and complement of gluing of two graphs. Key Words: Glued graph, Height, Big height, Krull dimension, Projective dimension, Linear resolution, Betti number, Cohen-Macaulay. INTRODUCTION The concept edge ideal was first introduced by Villarreal in Our paper is organized as follows. In section 2, we give an [23], that is, let be a simple (no loops or multiple edges) explicit formula for computing the height of glued graph at graph on the vertex set and the edge set complete clone. Also, we present a lower bound for the big . -
Topological Graph Persistence
CAIM ISSN 2038-0909 Commun. Appl. Ind. Math. 11 (1), 2020, 72–87 DOI: 10.2478/caim-2020-0005 Research Article Topological graph persistence Mattia G. Bergomi1*, Massimo Ferri2*, Lorenzo Zuffi2 1Veos Digital, Milan, Italy 2ARCES and Dept. of Mathematics, Univ. of Bologna, Italy *Email address for correspondence: [email protected] Communicated by Alessandro Marani Received on 04 28, 2020. Accepted on 11 09, 2020. Abstract Graphs are a basic tool in modern data representation. The richness of the topological information contained in a graph goes far beyond its mere interpretation as a one-dimensional simplicial complex. We show how topological constructions can be used to gain information otherwise concealed by the low-dimensional nature of graphs. We do this by extending previous work in homological persistence, and proposing novel graph-theoretical constructions. Beyond cliques, we use independent sets, neighborhoods, enclaveless sets and a Ramsey-inspired extended persistence. Keywords: Clique, independent set, neighborhood, enclaveless set, Ramsey AMS subject classification: 68R10, 05C10 1. Introduction Currently data are produced massively and rapidly. A large part of these data is either naturally organized, or can be represented, as graphs or networks. Recently, topological persistence has proved to be an invaluable tool for the exploration and understanding of these data types. Albeit graphs can be considered as topological objects per se, given their low dimensionality, only limited information can be obtained by studying their topology. It is possible to grasp more information by superposing higher-dimensional topological structures to a given graph. Many research lines, for ex- ample, focused on building n-dimensional complexes from graphs by considering their (n+1)-cliques (see, e.g., section 1.1). -
Parity Systems and the Delta-Matroid Intersection Problem
Parity Systems and the Delta-Matroid Intersection Problem Andr´eBouchet ∗ and Bill Jackson † Submitted: February 16, 1998; Accepted: September 3, 1999. Abstract We consider the problem of determining when two delta-matroids on the same ground-set have a common base. Our approach is to adapt the theory of matchings in 2-polymatroids developed by Lov´asz to a new abstract system, which we call a parity system. Examples of parity systems may be obtained by combining either, two delta- matroids, or two orthogonal 2-polymatroids, on the same ground-sets. We show that many of the results of Lov´aszconcerning ‘double flowers’ and ‘projections’ carry over to parity systems. 1 Introduction: the delta-matroid intersec- tion problem A delta-matroid is a pair (V, ) with a finite set V and a nonempty collection of subsets of V , called theBfeasible sets or bases, satisfying the following axiom:B ∗D´epartement d’informatique, Universit´edu Maine, 72017 Le Mans Cedex, France. [email protected] †Department of Mathematical and Computing Sciences, Goldsmiths’ College, London SE14 6NW, England. [email protected] 1 the electronic journal of combinatorics 7 (2000), #R14 2 1.1 For B1 and B2 in and v1 in B1∆B2, there is v2 in B1∆B2 such that B B1∆ v1, v2 belongs to . { } B Here P ∆Q = (P Q) (Q P ) is the symmetric difference of two subsets P and Q of V . If X\ is a∪ subset\ of V and if we set ∆X = B∆X : B , then we note that (V, ∆X) is a new delta-matroid.B The{ transformation∈ B} (V, ) (V, ∆X) is calledB a twisting. -
Persistent Homology of Unweighted Complex Networks Via Discrete
www.nature.com/scientificreports OPEN Persistent homology of unweighted complex networks via discrete Morse theory Received: 17 January 2019 Harish Kannan1, Emil Saucan2,3, Indrava Roy1 & Areejit Samal 1,4 Accepted: 6 September 2019 Topological data analysis can reveal higher-order structure beyond pairwise connections between Published: xx xx xxxx vertices in complex networks. We present a new method based on discrete Morse theory to study topological properties of unweighted and undirected networks using persistent homology. Leveraging on the features of discrete Morse theory, our method not only captures the topology of the clique complex of such graphs via the concept of critical simplices, but also achieves close to the theoretical minimum number of critical simplices in several analyzed model and real networks. This leads to a reduced fltration scheme based on the subsequence of the corresponding critical weights, thereby leading to a signifcant increase in computational efciency. We have employed our fltration scheme to explore the persistent homology of several model and real-world networks. In particular, we show that our method can detect diferences in the higher-order structure of networks, and the corresponding persistence diagrams can be used to distinguish between diferent model networks. In summary, our method based on discrete Morse theory further increases the applicability of persistent homology to investigate the global topology of complex networks. In recent years, the feld of topological data analysis (TDA) has rapidly grown to provide a set of powerful tools to analyze various important features of data1. In this context, persistent homology has played a key role in bring- ing TDA to the fore of modern data analysis. -
Arxiv:2006.02870V1 [Cs.SI] 4 Jun 2020
The why, how, and when of representations for complex systems Leo Torres Ann S. Blevins [email protected] [email protected] Network Science Institute, Department of Bioengineering, Northeastern University University of Pennsylvania Danielle S. Bassett Tina Eliassi-Rad [email protected] [email protected] Department of Bioengineering, Network Science Institute and University of Pennsylvania Khoury College of Computer Sciences, Northeastern University June 5, 2020 arXiv:2006.02870v1 [cs.SI] 4 Jun 2020 1 Contents 1 Introduction 4 1.1 Definitions . .5 2 Dependencies by the system, for the system 6 2.1 Subset dependencies . .7 2.2 Temporal dependencies . .8 2.3 Spatial dependencies . 10 2.4 External sources of dependencies . 11 3 Formal representations of complex systems 12 3.1 Graphs . 13 3.2 Simplicial Complexes . 13 3.3 Hypergraphs . 15 3.4 Variations . 15 3.5 Encoding system dependencies . 18 4 Mathematical relationships between formalisms 21 5 Methods suitable for each representation 24 5.1 Methods for graphs . 24 5.2 Methods for simplicial complexes . 25 5.3 Methods for hypergraphs . 27 5.4 Methods and dependencies . 28 6 Examples 29 6.1 Coauthorship . 29 6.2 Email communications . 32 7 Applications 35 8 Discussion and Conclusion 36 9 Acknowledgments 38 10 Citation diversity statement 38 2 Abstract Complex systems thinking is applied to a wide variety of domains, from neuroscience to computer science and economics. The wide variety of implementations has resulted in two key challenges: the progenation of many domain-specific strategies that are seldom revisited or questioned, and the siloing of ideas within a domain due to inconsistency of complex systems language. -
CS675: Convex and Combinatorial Optimization Spring 2018 Introduction to Matroid Theory
CS675: Convex and Combinatorial Optimization Spring 2018 Introduction to Matroid Theory Instructor: Shaddin Dughmi Set system: Pair (X ; I) where X is a finite ground set and I ⊆ 2X are the feasible sets Objective: often “linear”, referred to as modular Analogues of concave and convex: submodular and supermodular (in no particular order!) Today, we will look only at optimizing modular objectives over an extremely prolific family of set systems Related, directly or indirectly, to a large fraction of optimization problems in P Also pops up in submodular/supermodular optimization problems Optimization over Sets Most combinatorial optimization problems can be thought of as choosing the best set from a family of allowable sets Shortest paths Max-weight matching Independent set ... 1/30 Objective: often “linear”, referred to as modular Analogues of concave and convex: submodular and supermodular (in no particular order!) Today, we will look only at optimizing modular objectives over an extremely prolific family of set systems Related, directly or indirectly, to a large fraction of optimization problems in P Also pops up in submodular/supermodular optimization problems Optimization over Sets Most combinatorial optimization problems can be thought of as choosing the best set from a family of allowable sets Shortest paths Max-weight matching Independent set ... Set system: Pair (X ; I) where X is a finite ground set and I ⊆ 2X are the feasible sets 1/30 Analogues of concave and convex: submodular and supermodular (in no particular order!) Today, we will look only at optimizing modular objectives over an extremely prolific family of set systems Related, directly or indirectly, to a large fraction of optimization problems in P Also pops up in submodular/supermodular optimization problems Optimization over Sets Most combinatorial optimization problems can be thought of as choosing the best set from a family of allowable sets Shortest paths Max-weight matching Independent set .. -
FACE VECTORS of FLAG COMPLEXES 1. Introduction We
FACE VECTORS OF FLAG COMPLEXES ANDY FROHMADER Abstract. A conjecture of Kalai and Eckhoff that the face vector of an arbi- trary flag complex is also the face vector of some particular balanced complex is verified. 1. Introduction We begin by introducing the main result. Precise definitions and statements of some related theorems are deferred to later sections. The main object of our study is the class of flag complexes. A simplicial complex is a flag complex if all of its minimal non-faces are two element sets. Equivalently, if all of the edges of a potential face of a flag complex are in the complex, then that face must also be in the complex. Flag complexes are closely related to graphs. Given a graph G, define its clique complex C = C(G) as the simplicial complex whose vertex set is the vertex set of G, and whose faces are the cliques of G. The clique complex of any graph is itself a flag complex, as for a subset of vertices of a graph to not form a clique, two of them must not form an edge. Conversely, any flag complex is the clique complex of its 1-skeleton. The Kruskal-Katona theorem [6, 5] characterizes the face vectors of simplicial complexes as being precisely the integer vectors whose coordinates satisfy some par- ticular bounds. The graphs of the \rev-lex" complexes which attain these bounds invariably have a clique on all but one of the vertices of the complex, and sometimes even on all of the vertices. Since the bounds of the Kruskal-Katona theorem hold for all simplicial com- plexes, they must in particular hold for flag complexes. -
Matroid Intersection
EECS 495: Combinatorial Optimization Lecture 8 Matroid Intersection Reading: Schrijver, Chapter 41 • colorful spanning forests: for graph G with edges partitioned into color classes fE1;:::;Ekg, colorful spanning forest is Matroid Intersection a forest with edges of different colors. Define M1;M2 on ground set E with: Problem: { I1 = fF ⊂ E : F is acyclicg { I = fF ⊂ E : 8i; jF \ E j ≤ 1g • Given: matroids M1 = (S; I1) and M2 = 2 i (S; I ) on same ground set S 2 Then • Find: max weight (cardinality) common { these are matroids (graphic, parti- independent set J ⊆ I \I 1 2 tion) { common independent sets = color- Applications ful spanning forests Generalizes: Note: In second two examples, (S; I1 \I2) not a matroid, so more general than matroid • max weight independent set of M (take optimization. M = M1 = M2) Claim: Matroid intersection of three ma- troids NP-hard. • matching in bipartite graphs Proof: Reduction from directed Hamilto- For bipartite graph G = ((V1;V2);E), let nian path: given digraph D = (V; E) and Mi = (E; Ii) where vertices s; t, is there a path from s to t that goes through each vertex exactly once. Ii = fJ : 8v 2 Vi; v incident to ≤ one e 2 Jg • M1 graphic matroid of underlying undi- Then rected graph { these are matroids (partition ma- • M2 partition matroid in which F ⊆ E troids) indep if each v has at most one incoming edge in F , except s which has none { common independent sets = match- ings of G • M3 partition :::, except t which has none 1 Intersection is set of vertex-disjoint directed = r1(E) +r2(E)− min(r1(F ) +r2(E nF ) paths with one starting at s and one ending F ⊆E at t, so Hamiltonian path iff max cardinality which is number of vertices minus min intersection has size n − 1. -
Packing of Arborescences with Matroid Constraints Via Matroid Intersection
Mathematical Programming (2020) 181:85–117 https://doi.org/10.1007/s10107-019-01377-0 FULL LENGTH PAPER Series A Packing of arborescences with matroid constraints via matroid intersection Csaba Király1 · Zoltán Szigeti2 · Shin-ichi Tanigawa3 Received: 29 May 2018 / Accepted: 8 February 2019 / Published online: 2 April 2019 © The Author(s) 2019 Abstract Edmonds’ arborescence packing theorem characterizes directed graphs that have arc- disjoint spanning arborescences in terms of connectivity. Later he also observed a characterization in terms of matroid intersection. Since these fundamental results, intensive research has been done for understanding and extending these results. In this paper we shall extend the second characterization to the setting of reachability-based packing of arborescences. The reachability-based packing problem was introduced by Cs. Király as a common generalization of two different extensions of the spanning arborescence packing problem, one is due to Kamiyama, Katoh, and Takizawa, and the other is due to Durand de Gevigney, Nguyen, and Szigeti. Our new characterization of the arc sets of reachability-based packing in terms of matroid intersection gives an efficient algorithm for the minimum weight reachability-based packing problem, and it also enables us to unify further arborescence packing theorems and Edmonds’ matroid intersection theorem. For the proof, we also show how a new class of matroids can be defined by extending an earlier construction of matroids from intersecting submodular functions to bi-set functions based on an idea of Frank. Mathematics Subject Classification 05C05 · 05C20 · 05C70 · 05B35 · 05C22 B Csaba Király [email protected] Zoltán Szigeti [email protected] Shin-ichi Tanigawa [email protected] 1 Department of Operations Research, ELTE Eötvös Loránd University and the MTA-ELTE Egerváry Research Group on Combinatorial Optimization, Budapest Pázmány Péter sétány 1/C, 1117, Hungary 2 Univ. -
Percolation and Stochastic Topology NSF/CBMS Conference
Lecture 9: Percolation and stochastic topology NSF/CBMS Conference Sayan Mukherjee Departments of Statistical Science, Computer Science, Mathematics Duke University www.stat.duke.edu/ sayan ⇠ May 31, 2016 Stochastic topology “I predict a new subject of statistical topology. Rather than count the number of holes, Betti numbers, etc., one will be more interested in the distribution of such objects on noncompact manifolds as one goes out to infinity,” Isadore Singer. Random graphs Erd˝os-R´enyi G G(n, p) is a draw of a graph G from the graph distribution. ⇠ Consider p(n)andn and ask questions about thresholds of !1 graph properties. Erd˝os-R´enyi random graph model G(n, p) is the probability space of graphs on vertex set [n]= 1,...n with the probability of each edge p independently. { } Consider p(n)andn and ask questions about thresholds of !1 graph properties. Erd˝os-R´enyi random graph model G(n, p) is the probability space of graphs on vertex set [n]= 1,...n with the probability of each edge p independently. { } G G(n, p) is a draw of a graph G from the graph distribution. ⇠ Erd˝os-R´enyi random graph model G(n, p) is the probability space of graphs on vertex set [n]= 1,...n with the probability of each edge p independently. { } G G(n, p) is a draw of a graph G from the graph distribution. ⇠ Consider p(n)andn and ask questions about thresholds of !1 graph properties. Matthew Kahle (Ohio State University) Random topology and geometry AMS Short Course Matthew Kahle (Ohio State University) Random topology and geometry AMS Short Course Matthew