08/01/2021
10:00 - 11:00

ATI

Deep Weisfeiler Leman
Daniel Wiebking
We introduce the framework of Deep Weisfeiler Leman algorithms (DeepWL), which allows the design of purely combinatorial graph isomorphism tests that are more powerful than the well-known Weisfeiler-Leman algorithm.

We prove that, as an abstract computational model, polynomial-time DeepWL-algorithms have exactly the same expressiveness as the logic Choiceless Polynomial Time (with counting) introduced by Blass, Gurevich, and Shelah (Ann. Pure Appl. Logic., 1999).

It is a well-known open question whether the existence of a polynomial-time graph isomorphism test implies the existence of a polynomial-time canonisation algorithm. Our main technical result states that for each class of graphs (satisfying some mild closure condition), if there is a polynomial-time DeepWL isomorphism test, then there is a polynomial-time canonisation algorithm for this class. This implies that there is also a logic capturing polynomial time on this class.

18/12/2020
10:30 - 11:00

 

Bachelor-Vortrag: The Logical Structure of Probabilistic Databases
Jonas Lindner
As probabilistic databases (PDBs) may be very large, compact representation systems for these frameworks have been studied. But because complete representation systems (like pc-tables) can lead to very complex probability spaces, considering simpler and more intuitive non-complete representation systems, e.g. TI-tables or BID-tables, is of interest. These fundamental ’building blocks’ are especially interesting as they can be extended with views from the relational calculus to be more expressive. Similar to work done by Das Sarma et al. on representation systems for incomplete databases, this thesis introduces logical properties in order to separate the classes of PDBs that are representable with non-complete representation systems. The properties studied take a PDB’s underlying incomplete database as well as its probability distribu- tion into account and therefore help to grasp its logical structure. Having conducted this examination, convex combinations of probabilistic databases are studied, as they offer some advantages over the commonly considered representation systems regarding expressiveness.

12/11/2020
11:00 - 11:30

 

Connectivity and Routing using Graph Neural Networks
Frorian Frantzen
Graph Neural Networks form a deep learning architecture for machine learning tasks on graphs. Recently, they have been used to obtain heuristics for hard combinatorial problems such as TSP, SAT, or constraint satisfaction problems. The purpose of this thesis is to investigate how such approaches perform on computationally much easier shortest path problems. We will present lower bounds regarding the depth and width of a message passing neural network, which must be fulfilled for such a model to learn the shortest path problem. Then we will evaluate an existing neural network -- originally designed for the traveling salesperson problem -- for its applicability to the shortest path problem. While this network will fail with insignificant accuracies, these tests will allow us to derive unique challenges of the shortest path problem and which weaknesses in the architecture we need to fix to achieve better results. Based on these results, we will built our own neural network architecture and train it both supervised and unsupervised. We will evaluate this network in terms accuracy, scalability to larger graphs and how well the network generalizes the shortest path problem.

30/10/2020
10:00 - 11:00

ATI

Flag Algebras in Graph Theory
Tim Seppelt
Flag algebras as introduced by Razborov represent a powerful tool in extremal combinatorics. This talk will provide a gentle introduction to the framework highlighting applications graph theory. Moreover, limitations of flag algebraic techniques are discussed following the work of Hatami and Norine.

30/10/2020
10:00 - 11:00

ATI

Flag Algebras in Graph Theory
Tim Seppelt
Flag algebras as introduced by Razborov represent a powerful tool in extremal combinatorics. This talk will provide a gentle introduction to the framework highlighting applications graph theory. Moreover, limitations of flag algebraic techniques are discussed following the work of Hatami and Norine.

27/10/2020
14:00 - 14:30

BSc

The Fine-Grained Complexity of Longest Common Subsequence
Benjamin Stutte
[bachelor]We will examine approaches to fine-grained analyses of the Longest Common Subsequence (LCS) problem with special attention to recent results that have proven lower bounds for solving LCS under the Strong Exponential Time Hypothesis. We will first showcase two results, one by Abboud et al. (FOCS’15) and the other by Bringmann et al. (FOCS’15), where sub-quadratic lower bounds have been proven for the general LCS problem. Later, we turn to Bringmann et al.’s (SODA’18) which examined the optimal running time of multivariate LCS which proved lower bounds that coincide with the running times of the fastest known algorithms.

27/10/2020
14:45 - 15:15

BSc

Stable and Efficient Algorithms for Logarithmically Counting Homomorphisms from Selected Graph Classes
Anton Florey
Graph homomorphisms are mappings between two graphs H and G that preserve all edge relationsof the left-hand side graph. The graph homomorphism counting problem #Hom(H, G) asks for
the number of homomorphisms from one graph H ∈ H to another graph G ∈ G. It is polynomial
time solvable for certain left-hand graph classes H. As part of his recent master thesis, Maximilian
Merz already provided implementations of various homomorphism counting algorithms. Unfor-
tunately, they quickly get impracticable for large problem instances, as homomorphism numbers
grow extremely fast. This work revisits most of these algorithms with the novel idea of counting
the logarithm of these numbers. One main contribution is the implementation and evaluation of
these adapted algorithms. For most of the newly implemented functions, a significant improve-
ment in efficiency over the exact counting implementations can be measured. Further experiments
also show that errors of this logarithmic approximation stay sufficiently close to the unavoidable

accuracy loss induced by finite machine precision.
https://rwth.zoom.us/j/95988725888?pwd=NUlaM05VdU5Kdkp0NXVTZHplWlFSQT09

27/10/2020
14:00 - 14:30

BSc

The Fine-Grained Complexity of Longest Common Subsequence
Benjamin Stutte
[bachelor]We will examine approaches to fine-grained analyses of the Longest Common Subsequence (LCS) problem with special attention to recent results that have proven lower bounds for solving LCS under the Strong Exponential Time Hypothesis. We will first showcase two results, one by Abboud et al. (FOCS’15) and the other by Bringmann et al. (FOCS’15), where sub-quadratic lower bounds have been proven for the general LCS problem. Later, we turn to Bringmann et al.’s (SODA’18) which examined the optimal running time of multivariate LCS which proved lower bounds that coincide with the running times of the fastest known algorithms.

27/10/2020
14:45 - 15:15

BSc

Stable and Efficient Algorithms for Logarithmically Counting Homomorphisms from Selected Graph Classes
Anton Florey
Graph homomorphisms are mappings between two graphs H and G that preserve all edge relationsof the left-hand side graph. The graph homomorphism counting problem #Hom(H, G) asks for
the number of homomorphisms from one graph H ∈ H to another graph G ∈ G. It is polynomial
time solvable for certain left-hand graph classes H. As part of his recent master thesis, Maximilian
Merz already provided implementations of various homomorphism counting algorithms. Unfor-
tunately, they quickly get impracticable for large problem instances, as homomorphism numbers
grow extremely fast. This work revisits most of these algorithms with the novel idea of counting
the logarithm of these numbers. One main contribution is the implementation and evaluation of
these adapted algorithms. For most of the newly implemented functions, a significant improve-
ment in efficiency over the exact counting implementations can be measured. Further experiments
also show that errors of this logarithmic approximation stay sufficiently close to the unavoidable

accuracy loss induced by finite machine precision.
https://rwth.zoom.us/j/95988725888?pwd=NUlaM05VdU5Kdkp0NXVTZHplWlFSQT09

23/10/2020
10:30 - 11:00

MSc

Detecting Substructures with Graph Neural Networks
Michael Scholkemper
[master]We present theoretical results relating the computational expressiveness of graph neural networks (GNNs) to the color refinement algorithm for graph isomorphism (Morris et al., X et al.). These yield that given the wrong initialisation, graph neural networks are not able to detect triangles on regular graphs. We introduce a refinement algorithm for graph isomorphism that outperforms color refinement on regular graphs and propose a GNN-architecture motivated by this new algorithm. We experimentally investigate the effect of different initialisations on  the detection of triangles and extend this to k-cycles and k-cliques.

16/10/2020
10:00 - 11:00

ATI

Learning Concepts Described by Weight Aggregation Logic
Steffen van Bergerem
We consider weighted structures, which extend ordinary relational structures by assigning weights, i.e. elements from a particular group or ring, to tuples present in the structure. We introduce an extension of first-order logic that allows to aggregate weights of tuples, compare such aggregates, and use them to build more complex formulas. We provide locality properties of fragments of this logic including Feferman-Vaught decompositions and a Gaifman normal form for a fragment called FOW1, as well as a localisation theorem for a larger fragment called FOWA1. This fragment can express concepts from various machine learning scenarios. Using the locality properties, we show that concepts definable in FOWA1 over a weighted background structure of at most polylogarithmic degree are agnostically PAC-learnable in polylogarithmic time after pseudo-linear time preprocessing. This is joint work with Nicole Schweikardt.

13/10/2020
10:00 - 10:30

BSc

A comparison of translation from LTL to Büchi automata
Anna Maiworm

01/10/2020
10:00 - 10:45

 

Bachelor Vortrag Jan-Christoph Kassing
Vortrag zur Bachelorarbeit: The Recursive Algorithm for Parity Games

01/10/2020
10:00 - 10:45

 

Bachelor Vortrag Jan-Christoph Kassing
Vortrag zur Bachelorarbeit: The Recursive Algorithm for Parity Games

29/09/2020
10:00 - 10:45

 

Bachelor-Vortrag Johannes Lehmann
Vortrag zur Bachelorarbeit "Exact Minimization of omega-Automata"

29/09/2020
10:00 - 10:45

 

Bachelor-Vortrag Johannes Lehmann
Vortrag zur Bachelorarbeit "Exact Minimization of omega-Automata"

25/09/2020
10:30 - 11:00

MSc

Master-Vortrag: Graph Autoencoder for Chemical Compounds
Stefanie Winkler
https://rwth.zoom.us/j/91919735145?pwd=OWFJMDZ5aTErUjhLWEN0T1AwY0lIdz09 A challenging task in fuel design is to find suitable molecules that optimise desired properties like cetane and octane numbers. Our goal is to automate the generation of molecular graphs whose corresponding molecules serve as components for fuels with a high auto-ignition quality. We use three different neural network models built on variational autoencoders and a generative adversarial network: JT-VAE by Jin et al., MHG-VAE by Kajino and MolGAN by De Cao and Kipf [1–3]. Using a subset of QM9 for training, we adapt the models to optimise different combinations of the cetane and octane numbers of new molecules through Bayesian optimisation. Our results show that all models are able to produce valid molecules with high cetane and octane numbers. MHG-VAE is able to generate most of the highest scoring molecules in the different experiments. Moreover, it creates a lot more unique molecules than the other two models, many of which are easy to synthesise.

25/09/2020
11:15 - 12:00

BSc

Lower Bounds for the Weisfeiler-Leman Dimension of Graphs of Bounded Tree Width
Lea Schirp
The Weisfeiler-Leman (WL) algorithm is an iterative approach used to classify graphs and other relational structures. For every natural number k, there is a k-dimensional version, which colors k-tuples of vertices, running in polynomial time but there are also non-isomorphic graphs, which the k-dim version cannot distinguish. This makes it useful to know the WL-dimension of a graph, which is the least natural number k such that the k-dim WL distinguishes the graph from all other non-isomorphic graphs. It is known that bounds on the WL dimension of a graph can be determined if its tree width is known. In this talk, we present tools for computing the tree width of a graph with a focus on Tamaki's implementation. We finally discuss the results of an experimental evaluation of the tree width of these graphs with the goal of finding out if the currently best known bounds on the WL dimension of graphs parametrized by their tree width are tight.

25/09/2020
12:30 - 13:00

BSc

Counting Homomorphisms via Model Counting and Knowledge Compilation
Patrick Bögel
The homomorphism counting problem #HOM asks how many homomorphisms there are from a graph H to a graph G. The model counting problem #SAT asks how many satisfying assignments a propositional formula has. Both problems are #P-complete, but #HOM becomes tractable if the left-hand side graphs are restricted to a class of bounded tree-width. In this talk we consider reductions from #HOM to #SAT with special attention to whether the tractable subproblems of #HOM are mapped to tractable subproblems of #SAT. Specifically we find reductions that map homomorphism counting instances where the left-hand side graph is a tree, to formulas which adhere to structural restrictions under which #SAT becomes tractable. We also utilize the approach of knowledge compilation to create formulas in a representation that allows for the model count to be calculated in linear time of its size. We evaluate the reductions practically with multiple state-of-the-art model counters and find the reduction to be inferior to the direct approach.

18/09/2020
10:00 - 11:00

ATI

Tuple-Independent Representations of Probabilistic Databases
Christoph Standke
https://rwth.zoom.us/j/96525649453?pwd=bHRma0lmV2xuRTNhWUdQbi9ockdNZz09

03/08/2020
12:00 - 12:30

BSc

28/07/2020
16:00 - 17:00

 

28/07/2020
16:00 - 17:00

 

26/06/2020
10:00 - 11:00

ATI

ATI Seminar Jan Böker
Jan Böker

19/06/2020
10:00 - 10:45

ATI

ATI Seminar Vortrag Steffen van Bergerem
Steffen van Bergerem

05/06/2020
10:00 - 11:00

ATI

29/05/2020
10:00 - 10:30

ATI

Graph isomorphism in quasipolynomial time parameterized by treewidth
Daniel Wiebking

22/05/2020
10:00 - 11:00

ATI

The Iteration Number of Colour Refinement
Sandra Kiefer
The Colour Refinement procedure and its generalisation to higher dimensions, the Weisfeiler-Leman algorithm, are central subroutines in approaches to the graph isomorphism problem. In an iterative fashion, Colour Refinement computes a colouring of the vertices of its input graph. A trivial upper bound on the iteration number of Colour Refinement on graphs of order n is n-1. We show that this bound is tight. More precisely, we prove via explicit constructions that there are infinitely many graphs G on which Colour Refinement takes |G|-1 iterations to stabilise. Modifying the infinite families that we present, we show that for every natural number n ≥ 10, there are graphs on n vertices on which Colour Refinement requires at least n-2 iterations to reach stabilisation. This is joint work with Brendan McKay.

27/02/2020
10:00 - 10:30

MSc

Unsupervised machine learning for constraint satisfaction problems
Jan Tönshoff

27/02/2020
10:00 - 10:30

MSc

Unsupervised machine learning for constraint satisfaction problems
Jan Tönshoff

17/01/2020
10:00 - 11:00

ATI

Generative Datalog with Continuous Distributions
Peter Lindner
Probabilistic Databases (PDBs) are a formal model of uncertainty in relational databases, as might occur in a variety of practical application scenarios such as noisy or unreliable input data, data integration or data cleaning. Quite recently, Bárány et al. (TODS 2017) proposed a language called "Probabilistic Programming Datalog (PPDL)" which uses classic Datalog rules that are extended by random sampling. In a nutshell, PPDL is a declarative probabilistic programming language with very close ties to database applications and can be seen as a tool to specify PDBs. In this talk, we focus on the generative part of the language, "Generative Datalog". While the original language of Bárány et al. only supported discrete probability distributions, we allow using probability density functions and inputs that are already PDBs themselves. We present the formal semantics of the language and discuss various properties and consequences, most notably, the support of PDB inputs and robustness with respect to the order of rule applications. This is joint work with M. Grohe, B. L. Kaminski, J.-P. Katoen.

15/01/2020
10:00 - 10:30

MSc

Algorithms for counting homomorphisms from small treewidth graphs
Maximilian Merz

10/01/2020
10:00 - 11:00

ATI

Isomorphism Testing: From Strings to Hypergraphs
Daniel Neuen

We consider the isomorphism problem for hypergraphs taking as input two hypergraphs over the same set of vertices $V$ and a permutation group $Gamma$ over domain $V$, and asking whether there is a permutation $gamma in Gamma$ that proves the two hypergraphs to be isomorphic. We show that for input groups, all of whose composition factors are isomorphic to a subgroup of the symmetric group on $d$ points, this problem can be solved in time $(n+m)^{O((log d)^{c})}$ for some absolute constant $c$ where $n$ denotes the number of vertices and $m$ the number of hyperedges. The previous best algorithm for this problem due to Schweitzer and Wiebking (STOC 2019) runs in time $n^{O(d)}m^{O(1)}$.
 
As an application of this result, we obtain, for example, an algorithm testing isomorphism of graphs excluding $K_{3,h}$ ($h geq 3$) as a minor in time $n^{O((log h)^{c})}$. In particular, this gives an isomorphism test for graphs of Euler genus at most $g$ running in time $n^{O((log g)^{c})}$.

13/12/2019
10:00 - 11:00

ATI

RUN-CSP: Recurrent unsupervised network for CSPs
Martin Ritzert

Constraint satisfaction problems form an important and wide class of combinatorial search and optimization problems with many applications in AI and other areas. We introduce a recurrent neural network architecture RUN-CSP (Recurrent Unsupervised Neural Network for Constraint Satisfaction Problems) to train message passing networks solving binary constraint satisfaction problems (CSPs) or their optimization versions (Max-CSP). The architecture is universal in the sense that it works for all binary CSPs: depending on the constraint language, we can automatically design a loss function, which is then used to train generic neural nets. In this paper, we experimentally evaluate our approach for the 3-colorability problem (3-Col) and its optimization version (Max-3-Col) and for the maximum 2-satisfiability problem (Max-2-Sat). We also extend the framework to work for related optimization problems such as the maximum independent set problem (Max-IS). Training is unsupervised, we train the network on arbitrary (unlabeled) instances of the problems. Moreover, we experimentally show that it suffices to train on relatively small instances; the resulting message passing network will perform well on much larger instances (at least 10-times larger).

29/11/2019
10:00 - 11:00

ATI

29/11/2019
10:00 - 11:00

ATI

22/11/2019
10:00 - 11:00

ATI

Ambiguity in Probabilistic Büchi Automata
Anton Pirogov
Probabilistic Büchi automata are a natural generalization of PFA to infinite words, but have been studied in-depth only rather recently and many interesting questions are still open. PBA are known to accept, in general, a class of languages that goes beyond the regular languages. In this talk I will present new classes of restricted PBA which are still regular, strongly relying on notions concerning ambiguity in classical ω-automata.

20/11/2019
13:15 - 13:45

MSc

Algorithms for Maximum Matching Width
Yassin Bahloul

20/11/2019
15:00 - 15:30

BSc

The Fine-Grained Complexity of First-Order Properties
Louis Härtel
Based on the results by Jiawei Gao, Russell Impagliazzo, Antonina Kolokolova and Ryan Williams, we break down the fine-grained complexity of first-order properties by analyzing their reduction from any model-checking problem on a (k + 1)-quantifier first-order formula to the sparse version of k-Orthogonal Vectors, and summarize their algorithmic improvements and consequences for common fine-grained conjectures.

15/11/2019
10:00 - 11:00

ATI

25/10/2019
10:00 - 11:00

ATI

Tree automata with global constraints for infinite trees
Patrick Landwehr
We study an extension of tree automata on infinite trees with global equality and disequality constraints. These constraints can enforce that all subtrees for which in the accepting run a state q is reached (at the root of that subtree) are identical, or that these trees differ from the subtrees at which a state q' is reached. We consider the closure properties of this model, its decision problems and its connection to logic.

24/10/2019
10:00 - 10:30

BSc

Regular Sensing for Nested Word Automta
Alina Ibach

17/10/2019
10:00 - 10:30

BSc

Comparing learning algorithms for regular expressions from positive examples
Konrad Ostrowski

17/10/2019
10:45 - 11:15

BSc

A comparison of algorithms for automata learning on sparse data
Caspar Zecha

16/09/2019
11:00 - 11:30

 

The Complexity of First-Order Model Checking on Graphs of Bounded Tree Depth
Jonathan du Mesnil de Rochemont
In this talk, we discuss algorithmic meta-theorems for graphs of bounded tree-depth. Tree-Depth is a graph invariant also known as vertex ranking number and minimum elimination tree height, which captures, intuitively speaking, how much a graph resembles a star graph. We present a result due to Chen and Flum stating that the model-checking problem for FO parameterized by the length of the formula is in para-AC0 if the inputs are limited to any class of graphs of bounded tree-depth. para-AC0 can be viewed as the complexity class of parameterized problems that are decidable by polynomial-size constant-depth circuit families with arbitrary fan-in after a precomputation on the parameter. An important ingredient in the proof is the following characterization: We show that the model-checking problem for FO on any class of structures is in para-AC0 if and only if FO has an effective generalized quantifier elimination on that class. We then show that FO has such an effective generalized quantifier elimination on classes of graphs of bounded tree-depth to conclude the proof.

16/09/2019
11:45 - 12:15

 

Data structures for approximate membership queries
Razvan Manea

09/09/2019
14:00 - 15:00

 

Representations of Correlated Probabilistic Databases
Nils Freyer
As an extension of Codd's relational data model, probabilistic databases were discussed in the literature when the relational data model became popular in the 1980's. The studies of probabilistic databases were motivated by errors in the data collection process and designed as a generalisation of the relational data model that is able to integrate probabilistic data into relational data. Today, the amount of data and information is growing strictly, which causes a lot of research in information extraction and efforts in representing the extracted information. One can imagine that representing correlated probabilistic data is not trivial. We will present the most common representation systems for probabilistic data and examine their properties. Afterwards, we will construct translations between the representation systems to draw conclusions on their relationships.

14/06/2019
10:00 - 10:30

ATI

A unifying method for the design of algorithms canonizing combinatorial objects
Daniel Wiebking

We devise a unified framework for the design of canonization algorithms. 
Using hereditarily finite sets, we define a general notion of combinatorial objects 
that includes graphs, hypergraphs, relational structures, codes, permutation 
groups, tree decompositions, and so on. 
Our approach allows for a systematic transfer of the techniques that have been 
developed for isomorphism testing to canonization. We use it to design a canonization algorithm for general combinatorial objects. This result gives new fastest canonization algorithms with an asymptotic running time matching the best known isomorphism algorithm for the following types of objects: hypergraphs, hypergraphs of bounded color class size, permutation groups (up to permutational isomorphism) 
and codes that are explicitly given (up to code equivalence). 

31/05/2019
10:00 - 11:00

ATI

The Power of the Weisfeiler-Leman Algorithm to Decompose Graphs
Daniel Neuen

The Weisfeiler-Leman procedure is a widely-used approach for graph
isomorphism testing, which iteratively computes an isomorphism-invariant
coloring of vertex tuples. Meanwhile, a fundamental tool in structural
graph theory, which is often exploited in approaches to tackle the graph
isomorphism problem, is the decomposition into bi- and triconnected
components.
We prove that the 2-dimensional Weisfeiler-Leman algorithm implicitly
computes the decomposition of a graph into its triconnected components.
This means that the decomposition into triconnected components is "for
free" with respect to the dimension of the algorithm needed to
distinguish two graphs (assuming dimension at least 2).
This result implies that the k-dimensional algorithm distinguishes
k-separators, i.e., k-tuples of vertices that separate the graph, from
other vertex k-tuples. As a byproduct, we also obtain insights about the
connectivity of constituent graphs of association schemes.
In an application of the results, we show the new upper bound of k on
the Weisfeiler-Leman dimension of graphs of treewidth at most k. Using a
construction by Cai, Fürer, and Immerman, we also provide a new lower
bound that is asymptotically tight up to a factor of 2.
This is joint work with Sandra Kiefer.

31/05/2019
10:00 - 11:00

ATI

The Power of the Weisfeiler-Leman Algorithm to Decompose Graphs
Daniel Neuen

The Weisfeiler-Leman procedure is a widely-used approach for graph
isomorphism testing, which iteratively computes an isomorphism-invariant
coloring of vertex tuples. Meanwhile, a fundamental tool in structural
graph theory, which is often exploited in approaches to tackle the graph
isomorphism problem, is the decomposition into bi- and triconnected
components.
We prove that the 2-dimensional Weisfeiler-Leman algorithm implicitly
computes the decomposition of a graph into its triconnected components.
This means that the decomposition into triconnected components is "for
free" with respect to the dimension of the algorithm needed to
distinguish two graphs (assuming dimension at least 2).
This result implies that the k-dimensional algorithm distinguishes
k-separators, i.e., k-tuples of vertices that separate the graph, from
other vertex k-tuples. As a byproduct, we also obtain insights about the
connectivity of constituent graphs of association schemes.
In an application of the results, we show the new upper bound of k on
the Weisfeiler-Leman dimension of graphs of treewidth at most k. Using a
construction by Cai, Fürer, and Immerman, we also provide a new lower
bound that is asymptotically tight up to a factor of 2.
This is joint work with Sandra Kiefer.