10 research outputs found
A Breezing Proof of the KMW Bound
In their seminal paper from 2004, Kuhn, Moscibroda, and Wattenhofer (KMW)
proved a hardness result for several fundamental graph problems in the LOCAL
model: For any (randomized) algorithm, there are input graphs with nodes
and maximum degree on which (expected) communication rounds are
required to obtain polylogarithmic approximations to a minimum vertex cover,
minimum dominating set, or maximum matching. Via reduction, this hardness
extends to symmetry breaking tasks like finding maximal independent sets or
maximal matchings. Today, more than years later, there is still no proof
of this result that is easy on the reader. Setting out to change this, in this
work, we provide a fully self-contained and proof of the KMW
lower bound. The key argument is algorithmic, and it relies on an invariant
that can be readily verified from the generation rules of the lower bound
graphs.Comment: 21 pages, 6 figure
Beyond Flatland : exploring graphs in many dimensions
Societies, technologies, economies, ecosystems, organisms, . . . Our world is composed of complex networksâsystems with many elements that interact in nontrivial ways. Graphs are natural models of these systems, and scientists have made tremendous progress in developing tools for their analysis. However, research has long focused on relatively simple graph representations and problem specifications, often discarding valuable real-world information in the process. In recent years, the limitations of this approach have become increasingly apparent, but we are just starting to comprehend how more intricate data representations and problem formulations might benefit our understanding of relational phenomena. Against this background, our thesis sets out to explore graphs in five dimensions: descriptivity, multiplicity, complexity, expressivity, and responsibility. Leveraging tools from graph theory, information theory, probability theory, geometry, and topology, we develop methods to (1) descriptively compare individual graphs, (2) characterize similarities and differences between groups of multiple graphs, (3) critically assess the complexity of relational data representations and their associated scientific culture, (4) extract expressive features from and for hypergraphs, and (5) responsibly mitigate the risks induced by graph-structured content recommendations. Thus, our thesis is naturally situated at the intersection of graph mining, graph learning, and network analysis.Gesellschaften, Technologien, Volkswirtschaften, Ăkosysteme, Organismen, . . . Unsere Welt besteht aus komplexen NetzwerkenâSystemen mit vielen Elementen, die auf nichttriviale Weise interagieren. Graphen sind natĂźrliche Modelle dieser Systeme, und die Wissenschaft hat bei der Entwicklung von Methoden zu ihrer Analyse groĂe Fortschritte gemacht. Allerdings hat sich die Forschung lange auf relativ einfache Graphrepräsentationen und Problemspezifikationen beschränkt, oft unter Vernachlässigung wertvoller Informationen aus der realen Welt. In den vergangenen Jahren sind die Grenzen dieser Herangehensweise zunehmend deutlich geworden, aber wir beginnen gerade erst zu erfassen, wie unser Verständnis relationaler Phänomene von intrikateren Datenrepräsentationen und Problemstellungen profitieren kann. Vor diesem Hintergrund erkundet unsere Dissertation Graphen in fĂźnf Dimensionen: Deskriptivität, Multiplizität, Komplexität, Expressivität, und Verantwortung. Mithilfe von Graphentheorie, Informationstheorie, Wahrscheinlichkeitstheorie, Geometrie und Topologie entwickeln wir Methoden, welche (1) einzelne Graphen deskriptiv vergleichen, (2) Gemeinsamkeiten und Unterschiede zwischen Gruppen multipler Graphen charakterisieren, (3) die Komplexität relationaler Datenrepräsentationen und der mit ihnen verbundenen Wissenschaftskultur kritisch beleuchten, (4) expressive Merkmale von und fĂźr Hypergraphen extrahieren, und (5) verantwortungsvoll den Risiken begegnen, welche die Graphstruktur von Inhaltsempfehlungen mit sich bringt. Damit liegt unsere Dissertation naturgemäà an der Schnittstelle zwischen Graph Mining, Graph Learning und Netzwerkanalyse
Juristische Netzwerkforschung
Legal network science explores how legal phenomena can be represented as networks and investigates what can be gained from their quantification and visualization. Corinna Coupette introduces legal network science to the German legal discourse. Based on an original dataset of decisions by Germany's Federal Constitutional Court, she develops tools for modeling, measuring, and mapping the law.
The work includes an online appendix, which is freely available at :
https://doi.org/10.1628/978-3-16-157012-4-appendi
Evaluating the "Learning on Graphs" Conference Experience
With machine learning conferences growing ever larger, and reviewing
processes becoming increasingly elaborate, more data-driven insights into their
workings are required. In this report, we present the results of a survey
accompanying the first "Learning on Graphs" (LoG) Conference. The survey was
directed to evaluate the submission and review process from different
perspectives, including authors, reviewers, and area chairs alike
Complex Societies and the Growth of the Law
While a large number of informal factors influence how people interact,
modern societies rely upon law as a primary mechanism to formally control human
behaviour. How legal rules impact societal development depends on the interplay
between two types of actors: the people who create the rules and the people to
which the rules potentially apply. We hypothesise that an increasingly diverse
and interconnected society might create increasingly diverse and interconnected
rules, and assert that legal networks provide a useful lens through which to
observe the interaction between law and society. To evaluate these
propositions, we present a novel and generalizable model of statutory materials
as multidimensional, time-evolving document networks. Applying this model to
the federal legislation of the United States and Germany, we find impressive
expansion in the size and complexity of laws over the past two and a half
decades. We investigate the sources of this development using methods from
network science and natural language processing. To allow for cross-country
comparisons over time, we algorithmically reorganise the legislative materials
of the United States and Germany into cluster families that reflect legal
topics. This reorganisation reveals that the main driver behind the growth of
the law in both jurisdictions is the expansion of the welfare state, backed by
an expansion of the tax state.Comment: 22 pages, 6 figures (main paper); 28 pages, 11 figures (supplementary
information
Measuring Law Over Time: A Network Analytical Framework with an Application to Statutes and Regulations in the United States and Germany
How do complex social systems evolve in the modern world? This question lies
at the heart of social physics, and network analysis has proven critical in
providing answers to it. In recent years, network analysis has also been used
to gain a quantitative understanding of law as a complex adaptive system, but
most research has focused on legal documents of a single type, and there exists
no unified framework for quantitative legal document analysis using network
analytical tools. Against this background, we present a comprehensive framework
for analyzing legal documents as multi-dimensional, dynamic document networks.
We demonstrate the utility of this framework by applying it to an original
dataset of statutes and regulations from two different countries, the United
States and Germany, spanning more than twenty years (1998-2019). Our framework
provides tools for assessing the size and connectivity of the legal system as
viewed through the lens of specific document collections as well as for
tracking the evolution of individual legal documents over time. Implementing
the framework for our dataset, we find that at the federal level, the United
States legal system is increasingly dominated by regulations, whereas the
German legal system remains governed by statutes. This holds regardless of
whether we measure the systems at the macro, the meso, or the micro level.Comment: 32 pages, 13 figures (main paper); 32 pages, 14 figures
(supplementary information
Graph Similarity Description: How Are These Graphs Similar?
How do social networks differ across platforms? How do information networks change over time? Answering questions like these requires us to compare two or more graphs. This task is commonly treated as a measurement problem, but numerical answers give limited insight. Here, we argue that if the goal is to gain understanding, we should treat graph similarity assessment as a description problem instead. We formalize this problem as a model selection task using the Minimum Description Length principle, capturing the similarity of the input graphs in a common model and the differences between them in transformations to individual models. To discover good models, we propose Momo, which breaks the problem into two parts and introduces efficient algorithms for each. Through an extensive set of experiments on a wide range of synthetic and real-world graphs, we confirm that Momo works well in practic
Simplify Your Law: Using Information Theory to Deduplicate Legal Documents
Textual redundancy is one of the main challenges to ensuring that legal texts
remain comprehensible and maintainable. Drawing inspiration from the
refactoring literature in software engineering, which has developed methods to
expose and eliminate duplicated code, we introduce the duplicated phrase
detection problem for legal texts and propose the Dupex algorithm to solve it.
Leveraging the Minimum Description Length principle from information theory,
Dupex identifies a set of duplicated phrases, called patterns, that together
best compress a given input text. Through an extensive set of experiments on
the Titles of the United States Code, we confirm that our algorithm works well
in practice: Dupex will help you simplify your law.Comment: 8 pages, 3 figures; to appear in ICDMW 202
Differentially Describing Groups of Graphs
How does neural connectivity in autistic children differ from neural connectivity in healthy children or autistic youths? What patterns in global trade networks are shared across classes of goods, and how do these patterns change over time? Answering questions like these requires us to differentially describe groups of graphs: Given a set of graphs and a parti- tion of these graphs into groups, discover what graphs in one group have in common, how they systematically differ from graphs in other groups, and how multiple groups of graphs are related. We refer to this task as graph group analysis, which seeks to describe similarities and differences between graph groups by means of statistically significant subgraphs. To per- form graph group analysis, we introduce GRAGRA, which uses maximum entropy modeling to identify a non-redundant set of subgraphs with statistically significant associations to one or more graph groups. Through an extensive set of ex- periments on a wide range of synthetic and real-world graph groups, we confirm that GRAGRA works well in practice