107 research outputs found
LIPIcs, Volume 251, ITCS 2023, Complete Volume
LIPIcs, Volume 251, ITCS 2023, Complete Volum
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Foundations of Node Representation Learning
Low-dimensional node representations, also called node embeddings, are a cornerstone in the modeling and analysis of complex networks. In recent years, advances in deep learning have spurred development of novel neural network-inspired methods for learning node representations which have largely surpassed classical \u27spectral\u27 embeddings in performance. Yet little work asks the central questions of this thesis: Why do these novel deep methods outperform their classical predecessors, and what are their limitations?
We pursue several paths to answering these questions. To further our understanding of deep embedding methods, we explore their relationship with spectral methods, which are better understood, and show that some popular deep methods are equivalent to spectral methods in a certain natural limit. We also introduce the problem of inverting node embeddings in order to probe what information they contain. Further, we propose a simple, non-deep method for node representation learning, and find it to often be competitive with modern deep graph networks in downstream performance.
To better understand the limitations of node embeddings, we prove some upper and lower bounds on their capabilities. Most notably, we prove that node embeddings are capable of exact low-dimensional representation of networks with bounded max degree or arboricity, and we further show that a simple algorithm can find such exact embeddings for real-world networks. By contrast, we also prove inherent bounds on random graph models, including those derived from node embeddings, to capture key structural properties of networks without simply memorizing a given graph
Percolated stochastic block model via EM algorithm and belief propagation with non-backtracking spectra
Whereas Laplacian and modularity based spectral clustering is apt to dense
graphs, recent results show that for sparse ones, the non-backtracking spectrum
is the best candidate to find assortative clusters of nodes. Here belief
propagation in the sparse stochastic block model is derived with arbitrary
given model parameters that results in a non-linear system of equations; with
linear approximation, the spectrum of the non-backtracking matrix is able to
specify the number of clusters. Then the model parameters themselves can be
estimated by the EM algorithm. Bond percolation in the assortative model is
considered in the following two senses: the within- and between-cluster edge
probabilities decrease with the number of nodes and edges coming into existence
in this way are retained with probability . As a consequence, the
optimal is the number of the structural real eigenvalues (greater than
, where is the average degree) of the non-backtracking matrix of
the graph. Assuming, these eigenvalues are distinct, the
multiple phase transitions obtained for are ; further, at the number of detectable clusters is
, for . Inflation-deflation techniques are also discussed to
classify the nodes themselves, which can be the base of the sparse spectral
clustering.Comment: 29 pages, 16 figure
Multistage Shortest Path: Instances and Practical Evaluation
A multistage graph problem is a generalization of a traditional graph problem where, instead of a single input graph, we consider a sequence of graphs. We ask for a sequence of solutions, one for each input graph, such that consecutive solutions are as similar as possible. There are several theoretical results on different multistage problems and their complexities, as well as FPT and approximation algorithms. However, there is a severe lack of experimental validation and resulting feedback. Not only are there no algorithmic experiments in literature, we do not even know of any strong set of multistage benchmark instances.
In this paper we want to improve on this situation. We consider the natural problem of multistage shortest path (MSP). First, we propose a rich benchmark set, ranging from synthetic to real-world data, and discuss relevant aspects to ensure non-trivial instances, which is a surprisingly delicate task. Secondly, we present an explorative study on heuristic, approximate, and exact algorithms for the MSP problem from a practical point of view. Our practical findings also inform theoretical research in arguing sensible further directions. For example, based on our study we propose to focus on algorithms for multistage instances that do not rely on 2-stage oracles
Mixing and localisation in time-periodic quantum circuits
This thesis introduces and analyses a new model of time-periodic (Floquet) dynamics
in a quantum spin systems. This model is implemented via a time-periodic quantum
circuit with local Clifford gates. All the results of this thesis are rigorous mathematical proofs, which use tools and methods from quantum information science
to study problems in many-body quantum systems and condensed-matter physics.
This includes proofs of a form of dynamical mixing of Pauli operators in the case of
local interactions, and conditions under which the evolution operator can resemble a
random unitary. The scrambling time is of critical importance to these results, and in
the case of non-local interactions, a slightly larger than logarithmic scrambling time
is found. Also, the model analysed in this thesis has the peculiarity that it displays a
strong form of localisation in one spatial dimension and the absence of localisation in
two dimensions. There is no previously known model with these features, hence, this
research is important to characterise the landscape of many-body quantum physics
Statistical Analysis of Networks
This book is a general introduction to the statistical analysis of networks, and can serve both as a research monograph and as a textbook. Numerous fundamental tools and concepts needed for the analysis of networks are presented, such as network modeling, community detection, graph-based semi-supervised learning and sampling in networks. The description of these concepts is self-contained, with both theoretical justifications and applications provided for the presented algorithms.
Researchers, including postgraduate students, working in the area of network science, complex network analysis, or social network analysis, will find up-to-date statistical methods relevant to their research tasks. This book can also serve as textbook material for courses related to the
statistical approach to the analysis of complex networks.
In general, the chapters are fairly independent and self-supporting, and the book could be used for course composition âĂ la carteâ. Nevertheless, Chapter 2 is needed to a certain degree for all parts of the book. It is also recommended to read Chapter 4 before reading Chapters 5 and 6, but this is not absolutely necessary. Reading Chapter 3 can also be helpful before reading Chapters 5 and 7.
As prerequisites for reading this book, a basic knowledge in probability, linear algebra and elementary notions of graph theory is advised. Appendices describing required notions from the above mentioned disciplines have been added to help readers gain further understanding
Clique Factors: Extremal and Probabilistic Perspectives
A K_r-factor in a graph G is a collection of vertex-disjoint copies of K_r covering the vertex set of G. In this thesis, we investigate these fundamental objects in three settings that lie at the intersection of extremal and probabilistic combinatorics.
Firstly, we explore pseudorandom graphs. An n-vertex graph is said to be (p,ÎČ)-bijumbled if for any vertex sets A, B â V (G), we have e( A, B) = p| A||B| ± ÎČâ|A||B|. We prove that for any 3 †r â N and c > 0 there exists an Δ > 0 such that any n-vertex (p, ÎČ)-bijumbled graph with n â rN, ÎŽ(G) â„ c p n and ÎČ â€ Î” p^{r â1} n, contains a K_r -factor. This implies a corresponding result for the stronger pseudorandom notion of (n, d, λ)-graphs. For the case of K_3-factors, this result resolves a conjecture of Krivelevich, Sudakov and SzabĂł from 2004 and it is tight due to a pseudorandom triangle-free construction of Alon. In fact, in this case even more is true: as a corollary to this result, we can conclude that the same condition of ÎČ = o( p^2n) actually guarantees that a (p, ÎČ)-bijumbled graph G contains every graph on n vertices with maximum degree at most 2.
Secondly, we explore the notion of robustness for K_3-factors. For a graph G and p â [0, 1], we denote by G_p the random sparsification of G obtained by keeping each edge of G independently, with probability p. We show that there exists a C > 0 such that if p â„ C (log n)^{1/3}n^{â2/3} and G is an n-vertex graph with n â 3N and ÎŽ(G) â„ 2n/3 , then with high probability G_p contains a K_3-factor. Both the minimum degree condition and the probability condition, up to the choice of C, are tight. Our result can be viewed as a common strengthening of the classical extremal theorem of CorrĂĄdi and Hajnal, corresponding to p = 1 in our result, and the famous probabilistic theorem of Johansson, Kahn and Vu establishing the threshold for the appearance of K_3-factors (and indeed all K_r -factors) in G (n, p), corresponding to G = K_n in our result. It also implies a first lower bound on the number of K_3-factors in graphs with minimum degree at least 2n/3, which gets close to the truth.
Lastly, we consider the setting of randomly perturbed graphs; a model introduced by Bohman, Frieze and Martin, where one starts with a dense graph and then adds random edges to it. Specifically, given any fixed 0 < α < 1 â 1/r we determine how many random edges one must add to an n-vertex graph G with ÎŽ(G) ℠α n to ensure that, with high probability, the resulting graph contains a K_r -factor. As one increases α we demonstrate that the number of random edges
required âjumpsâ at regular intervals, and within these intervals our result is best-possible. This work therefore bridges the gap between the seminal work of Johansson, Kahn and Vu mentioned above, which resolves the purely random case, i.e., α = 0, and that of Hajnal and SzemerĂ©di (and CorrĂĄdi and Hajnal for r = 3) showing that when α â„ 1 â 1/r the initial graph already hosts the
desired K_r -factor.Ein K_r -Faktor in einem Graphen G ist eine Sammlung von Knoten-disjunkten Kopien von K_r , die die Knotenmenge von G ĂŒberdecken. Wir untersuchen diese Objekte in drei Kontexten, die an der Schnittstelle zwischen extremaler und probabilistischer Kombinatorik liegen.
Zuerst untersuchen wir Pseudozufallsgraphen. Ein Graph heiĂt (p,ÎČ)-bijumbled, wenn fĂŒr beliebige Knotenmengen A, B â V (G) gilt e( A, B) = p| A||B| ± ÎČâ|A||B|. Wir beweisen, dass es fĂŒr jedes 3 †r â N und c > 0 ein Δ > 0 gibt, so dass jeder n-Knoten (p, ÎČ)-bijumbled Graph mit n â rN, ÎŽ(G) â„ c p n und ÎČ â€ Î” p^{r â1} n, einen K_r -Faktor enthĂ€lt. Dies impliziert ein entsprechendes Ergebnis fĂŒr den stĂ€rkeren Pseudozufallsbegriff von (n, d, λ)-Graphen. Im Fall von K_3-Faktoren, löst dieses Ergebnis eine Vermutung von Krivelevich, Sudakov und SzabĂł aus
dem Jahr 2004 und ist durch eine pseudozufĂ€llige K_3-freie Konstruktion von Alon bestmöglich. TatsĂ€chlich ist in diesem Fall noch mehr wahr: als Korollar dieses Ergebnisses können wir schlieĂen, dass die gleiche Bedingung von ÎČ = o( p^2n) garantiert, dass ein (p, ÎČ)-bijumbled Graph G jeden Graphen mit maximalem Grad 2 enthĂ€lt.
Zweitens untersuchen wir den Begriff der Robustheit fĂŒr K_3-Faktoren. FĂŒr einen Graphen G und p â [0, 1] bezeichnen wir mit G_p die zufĂ€llige Sparsifizierung von G, die man erhĂ€lt, indem man jede Kante von G unabhĂ€ngig von den anderen Kanten mit einer Wahrscheinlichkeit p behĂ€lt. Wir zeigen, dass, wenn p â„ C (log n)^{1/3}n^{â2/3} und G ein n-Knoten-Graph mit n â 3N und ÎŽ(G) â„ 2n/3 ist, G_pmit hoher Wahrscheinlichkeit (mhW) einen K_3-Faktor enthĂ€lt. Sowohl die Bedingung des minimalen Grades als auch die Wahrscheinlichkeitsbedingung sind bestmöglich. Unser Ergebnis ist eine VerstĂ€rkung des klassischen extremalen Satzes von CorrĂĄdi und Hajnal, entsprechend p = 1 in unserem Ergebnis, und des berĂŒhmten probabilistischen Satzes von Johansson, Kahn und Vu, der den Schwellenwert fĂŒr das Auftreten eines K_3-Faktors (und aller K_r -Faktoren) in G (n, p) festlegt, entsprechend G = K_n in unserem Ergebnis. Es impliziert auch eine erste untere Schranke fĂŒr die Anzahl der K_3-Faktoren in Graphen mit einem minimalen Grad von mindestens 2n/3, die der Wahrheit nahe kommt.
SchlieĂlich betrachten wir die Situation von zufĂ€llig gestörten Graphen; ein Modell, bei dem man mit einem dichten Graphen beginnt und dann zufĂ€llige Kanten hinzufĂŒgt. Wir bestimmen, bei gegebenem 0 < α < 1 â 1/r, wie viele zufĂ€llige Kanten man zu einem n-Knoten-Graphen G mit ÎŽ(G) ℠α n hinzufĂŒgen muss, um sicherzustellen, dass der resultierende Graph mhW einen K_r -Faktor enthĂ€lt. Wir zeigen, dass, wenn man α erhöht, die Anzahl der benötigten Zufallskanten in regelmĂ€Ăigen AbstĂ€nden âspringt", und innerhalb dieser AbstĂ€nde unser Ergebnis bestmöglich ist. Diese Arbeit schlieĂt somit die LĂŒcke zwischen der oben erwĂ€hnten bahnbrechenden Arbeit von Johansson, Kahn und Vu, die den rein zufĂ€lligen Fall, d.h. α = 0, löst, und der Arbeit von Hajnal und SzemerĂ©di (und CorrĂĄdi und Hajnal fĂŒr r = 3), die zeigt, dass der ursprĂŒngliche Graph bereits den gewĂŒnschten K_r -Faktor enthĂ€lt, wenn α â„ 1 â 1/r ist
Irreducibility of the Tutte polynomial of an embedded graph
We prove that the ribbon graph polynomial of a graph embedded in
an orientable surface is irreducible if and only if the embedded graph is neither the disjoint union nor the join of embedded graphs. This result is analogous to the fact that the Tutte polynomial of a graph is irreducible if and only if the graph is connected and non-separable
On Algorithmic Fairness and Stochastic Models for Combinatorial Optimization and Unsupervised Machine Learning
Combinatorial optimization and unsupervised machine learning problems have been extensively studied and are relatively well-understood. Examples of such problems that play a central role in this work are clustering problems and problems of finding cuts in graphs. The goal of the research presented in this dissertation is to introduce novel variants of the aforementioned problems, by generalizing their classic variants into two, not necessarily disjoint, directions. The first direction involves incorporating fairness aspects to a problem's specifications, and the second involves the introduction of some form of randomness in the problem definition, e.g., stochastic uncertainty about the problem's parameters.
Fairness in the design of algorithms and in machine learning has received a significant amount of attention during the last few years, mainly due to the realization that standard optimization approaches can frequently lead to severely unfair outcomes, that can potentially hurt the individuals or the groups involved in the corresponding application. As far as considerations of fairness are concerned, in this work we begin by presenting two novel individually-fair clustering models, together with algorithms with provable guarantees for them. The first such model exploits randomness in order to provide fair solutions, while the second is purely deterministic. The high-level motivation behind both of them is trying to treat similar individuals similarly. Moving forward, we focus on a graph cut problem that captures situations of disaster containment in a network. For this problem we introduce two novel fair variants. The first variant focuses on demographic fairness, while the second considers a probabilistic notion of individual fairness. Again, we give algorithms with provable guarantees for the newly introduced variants.
In the next part of this thesis we turn our attention to generalizing problems through the introduction of stochasticity. At first, we present algorithmic results for a computational epidemiology problem, whose goal is to control the stochastic diffusion of a disease in a contact network. This problem can be interpreted as a stochastic generalization of a static graph cut problem. Finally, this dissertation also includes work on a well-known paradigm in stochastic optimization, namely the two-stage stochastic setting with recourse. Two-stage problems capture a wide variety of applications revolving around the trade-off between provisioning and rapid response. In this setting, we present a family of clustering problems that had not yet been studied in the literature, and for this family we show novel algorithmic techniques that provide constant factor approximation algorithms.
We conclude the dissertation with a discussion on open problems and future research directions in the general area of algorithmic fairness
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