See also Google Scholar or DBLP
(Ordered by authors' last names).
Authors  Title  Journal  Conference  arxiv  anno 

Cristina Bazgan, TF, André Nichterlein, Rolf Niedermeier, and Maximilian Stahlberg  A More FineGrained Complexity Analysis of Finding the Most Vital Edges for Undirected Shortest Paths  Networks 73(1):2337 (2019)  arxiv  2018  
Matthias Bentert, René van Bevern, TF, André Nichterlein, Rolf Niedermeier  PolynomialTime Preprocessing for Weighted Problems Beyond Additive Goal Functions  under review  arXiv ✎  2019  
Matthias Bentert, TF, André Nichterlein, and 
Parameterized Aspects of Triangle Enumeration
abstractListing all triangles in an undirected graph is a fundamental graph primitive with numerous applications. It is trivially solvable in time cubic in the number of vertices. It has seen a significant body of work contributing to both theoretical aspects (e.g., lower and upper bounds on running time, adaption to new computational models) as well as practical aspects (e.g. algorithms tuned for large graphs). Motivated by the fact that the worstcase running time is cubic, we perform a systematic parameterized complexity study of triangle enumeration, providing both positive results (new enumerative kernelizations, “subcubic” parameterized solving algorithms) as well as negative results (uselessness in terms of possibility of “faster” parameterized algorithms of certain parameters such as diameter). 
JCSS(SI) 103:6177 (2019)  FCT 2017: 10472:96–110  arXiv ✎  2017 
René van Bevern, TF, George B. Mertzios, 
The parameterized complexity of finding secluded solutions to some classical optimization problems on graphs  Discrete Optimization 30: 2050 (2018)  IPEC 2016: 5:15:16  arXiv  2016 
René van Bevern, TF, Oxana Yu. Tsidulko  Parameterized algorithms and data reduction for the short secluded stpath problem
abstractGiven a graph G=(V,E), two vertices s,t in V, and two integers k,l, the Short Secluded Path problem is to find a simple stpath with at most k vertices and l neighbors. We study the parameterized complexity of the problem with respect to four structural graph parameters: the vertex cover number, treewidth, feedback vertex number, and feedback edge number. In particular, we completely settle the question of the existence of problem kernels with size polynomial in these parameters and their combinations with k and l. We also obtain a 2^O(w) l^2 ntime algorithm for graphs of treewidth w, which yields subexponentialtime algorithms in several graph classes. 
Networks 75(1): 3463 (2020)  ATMOS 2018: 10:110:19 ✎  arxiv  2018 
René van Bevern, TF, Oxana Yu. Tsidulko  On (1+ε)approximate problem kernels for the Rural Postman Problem  under review  MOTOR 2019: 279294  arXiv  2018 
Robert Bredereck, TF, Andrzej Kaczmarczyk  Multistage Committee Election
abstractElecting a single committee of a small size is a classical and wellunderstood voting situation. Being interested in a sequence of committees, we introduce and study two timedependent multistage models based on simple Plurality voting. Therein, we are given a sequence of voting profiles (stages) over the same set of agents and candidates, and our task is to find a small committee for each stage of high score. In the conservative model we additionally require that any two consecutive committees have a small symmetric difference. Analogously, in the revolutionary model we require large symmetric differences. We prove both models to be NPhard even for a constant number of agents, and, based on this, initiate a parameterized complexity analysis for the most natural parameters and combinations thereof. Among other results, we prove both models to be in XP yet W[1]hard regarding the number of stages, and that being revolutionary seems to be "easier" than being conservative: If the (upper resp. lower) bound on the size of symmetric differences is constant, the conservative model remains NPhard while the revolutionary model becomes polynomialtime solvable. 
arXiv  2020  
Markus Brill, TF, Vincent Froese, Brijnesh Jain, Rolf Niedermeier, and David Schultz  Exact Mean Computation in Dynamic Time Warping Spaces
abstractDynamic time warping constitutes a major tool for analyzing time series. In particular, computing a mean series of a given sample of series in dynamic time warping spaces (by minimizing the Fr\'echet function) is a challenging computational problem, so far solved by several heuristic, inexact strategies. We spot several inaccuracies in the literature on exact mean computation in dynamic time warping spaces. Our contributions comprise an exact dynamic program computing a mean (useful for benchmarking and evaluating known heuristics). Empirical evaluations reveal significant deficits of the stateoftheart heuristics in terms of their output quality. Finally, we give an exact polynomialtime algorithm for the special case of binary time series. 
Data Min. Knowl. Discov. 33(1): 252291  SDM 2018: 540–548 ✎  arXiv  2017 
Henning Fernau, TF, Danny Hermelin, 
Diminishable Parameterized Problems and Strict Polynomial Kernelization  Computability, vol. 9, no. 1, pp. 124, 2020  CiE 2018: 10936:161–171 ✎  arXiv  2016 
TF, Meike Hatzel, Steffen Härtlein, Hendrik Molter, and Henning Seidler  The Minimum Shared Edges Problem on Gridlike Graphs
abstractWe study the 𝖭𝖯hard Minimum Shared Edges (MSE) problem on graphs: decide whether it is possible to route p paths from a start vertex to a target vertex in a given graph while using at most k edges more than once. We show that MSE can be decided on bounded (i.e. finite) grids in linear time when both dimensions are either small or large compared to the number p of paths. On the contrary, we show that MSE remains 𝖭𝖯hard on subgraphs of bounded grids. Finally, we study MSE from a parametrised complexity point of view. It is known that MSE is fixedparameter tractable with respect to the number p of paths. We show that, under standard complexitytheoretical assumptions, the problem parametrised by the combined parameter k, p, maximum degree, diameter, and treewidth does not admit a polynomialsize problem kernel, even when restricted to planar graphs. 
WG 2017: 10520:249–262  arXiv  2017  
TF, Danny Hermelin, André Nichterlein, and 
Fractals for Kernelization Lower Bounds  SIAM J. Discrete Math., 32(1), 656681 (2018) ✎  ICALP 2016: (55)25:125:14 ✎  arxiv ✎  2015 
TF, Christian Komusiewicz, George B. Mertzios, André Nichterlein, Rolf Niedermeier, and 
When can Graph Hyperbolicity be computed in Linear Time?  Algorithmica 81(5): 20162045 (2019)  WADS 2017: 10389:397–408  arXiv  2017 
TF, Stefan Kratsch, Rolf Niedermeier, and 
The Parameterized Complexity of the Minimum Shared Edges Problem  JCSS  FSTTCS 2015: 448462  arxiv  2016 
TF, Steffen Kriewald, Anselmo García Cantú Ros, Bin Zhou, Dominik E. Reusser, Jürgen P. Kropp, and Diego Rybski  The Size Distribution, Scaling Properties and Spatial Organization of Urban Clusters: A Global and Regional Perspective  IJGI, ISPRS Int. J. GeoInf. 2016, 5(7), 110  arxiv  2014  
TF, George B. Mertzios, and André Nichterlein  Kernelization Lower Bounds for Finding Constant Size Subgraphs
abstractKernelization is an important tool in parameterized algorithmics. The goal is to reduce the input instance of a parameterized problem in polynomial time to an equivalent instance of the same problem such that the size of the reduced instance only depends on the parameter and not on the size of the original instance. In this paper, we provide a first conceptual study on limits of kernelization for several polynomialtime solvable problems. For instance, we consider the problem of finding a triangle with negative sum of edge weights parameterized by the maximum degree of the input graph. We prove that a lineartime computable strict kernel of truly subcubic size for this problem violates the popular APSPconjecture. 
CiE 2018: 10936:183–193 ✎  arXiv  2017  
TF, Hendrik Molter, Rolf Niedermeier, Malte Renken, and Philipp Zschoche  Temporal Graph Classes: A View Through Temporal Separators
abstractWe investigate the computational complexity of separating two distinct vertices s and z by vertex deletion in a temporal graph. In a temporal graph, the vertex set is fixed but the edges have (discrete) time labels. Since the corresponding Temporal (s, z)Separation problem is NPhard, it is natural to investigate whether relevant special cases exist that are computationally tractable. To this end, we study restrictions of the underlying (static) graphthere we observe polynomialtime solvability in the case of bounded treewidthas well as restrictions concerning the "temporal evolution" along the time steps. Systematically studying partially novel concepts in this direction, we identify sharp borders between tractable and intractable cases. 
Theor. Comput. Sci. 806: 197218 (2020)  WG 2018: 216227 ✎  arXiv  2018 
TF, Hendrik Molter, Rolf Niedermeier, Malte Renken, and Philipp Zschoche  As Time Goes By: Reflections on Treewidth for Temporal Graphs
abstractTreewidth is arguably the most important structural graph parameter leading to algorithmically beneficial graph decompositions. Triggered by a strongly growing interest in temporal networks (graphs where edge sets change over time), we discuss fresh algorithmic views on temporal tree decompositions and temporal treewidth. We review and explain some of the recent work together with some encountered pitfalls, and we point out challenges for future research. 
Treewidth, Kernels, and Algorithms 2020: 4977 ✎  arXiv  2018  
TF, Marco Morik, and Manuel Sorge  The Complexity of Routing with Few Collisions
abstractWe study the computational complexity of routing multiple objects through a network in such a way that only few collisions occur: Given a graph G with two distinct terminal vertices and two positive integers p and k, the question is whether one can connect the terminals by at least p routes (e.g. paths) such that at most k edges are timewise shared among them. We study three types of routes: traverse each vertex at most once (paths), each edge at most once (trails), or no such restrictions (walks). We prove that for paths and trails the problem is NPcomplete on undirected and directed graphs even if k is constant or the maximum vertex degree in the input graph is constant. For walks, however, it is solvable in polynomial time on undirected graphs for arbitrary k and on directed graphs if k is constant. We additionally study for all route types a variant of the problem where the maximum length of a route is restricted by some given upper bound. We prove that this lengthrestricted variant has the same complexity classification with respect to paths and trails, but for walks it becomes NPcomplete on undirected graphs. 
JCSS(SI) 102: 6986 (2019)  FCT 2017: 10472:257–270  arXiv ✎  2017 
TF, Rolf Niedermeier, Valentin Rohm, and Philipp Zschoche  Multistage Vertex Cover
abstractCovering all edges of a graph by a minimum number of vertices, this is the NPhard Vertex Cover problem, is among the most fundamental algorithmic tasks. Following a recent trend in studying dynamic and temporal graphs, we initiate the study of Multistage Vertex Cover. Herein, having a series of graphs with same vertex set but over time changing edge sets (known as temporal graph consisting of various layers), the goal is to find for each layer of the temporal graph a small vertex cover and to guarantee that the two vertex cover sets between two sub sequent layers differ not too much (specified by a given parameter). We show that, different from classic Vertex Cover and some other dynamic or temporal variants of it, Multistage Vertex Cover is computationally hard even in fairly restricted settings. On the positive side, however, we also spot several fixedparameter tractability results based on some of the most natural parameterizations. 
under review  IPEC 2019: 14:114:14  arxiv  2019 
TF, Rolf Niedermeier, Carsten Schubert, Philipp Zschoche  Multistage st Path: Confronting Similarity with Dissimilarity
abstractAddressing a quest by Gupta et al. [ICALP'14], we provide a first, comprehensive study of finding a short st path in the multistage graph model, referred to as the Multistage st Path problem. Herein, given a sequence of graphs over the same vertex set but changing edge sets, the task is to find short st paths in each graph ("snapshot") such that in the resulting path sequence the consecutive st paths are "similar". We measure similarity by the size of the symmetric difference of either the vertex set (vertexsimilarity) or the edge set (edgesimilarity) of any two consecutive paths. We prove that the two variants of Multistage st Path are already NPhard for an input sequence of only two graphs. Motivated by this fact and natural applications of this scenario e.g. in traffic route planning, we perform a parameterized complexity analysis. Among other results, we prove parameterized hardness (W[1]hardness) regarding the size of the path sequence (solution size) for both variants, vertex and edgesimilarity. As a novel conceptual contribution, we then modify the multistage model by asking for dissimilar consecutive paths. As one of the main results, we prove that dissimilarity allows for fixedparameter tractability for the parameter solution size, thereby contrasting our W[1]hardness proof of the corresponding similarity case. 
arxiv  2020  
TF, Piotr Skowron, Mervin Triphaus, Kai Wilker  Fair Knapsack
abstractWe study the following multiagent variant of the knapsack problem. We are given a set of items, a set of voters, and a value of the budget; each item is endowed with a cost and each voter assigns to each item a certain value. The goal is to select a subset of items with the total cost not exceeding the budget, in a way that is consistent with the voters' preferences. Since the preferences of the voters over the items can vary significantly, we need a way of aggregating these preferences, in order to select the socially most preferred valid knapsack. We study three approaches to aggregating voters preferences, which are motivated by the literature on multiwinner elections and fair allocation. This way we introduce the concepts of individually best, diverse, and fair knapsack. We study computational complexity (including parameterized complexity, and complexity under restricted domains) of computing the aforementioned concepts of multiagent knapsacks. 
AAAI 2019 33(1):19411948  arXiv  2017  
TF, Manuel Sorge  The Minimum Shared Edges Problem on Planar Graphs  arXiv  2016  
Ramana Venkata Gudipudi, TF, Anselmo García Cantú Ros, Carsten Walther, and Jürgen P. Kropp  City Density and CO2 Efficiency
abstractCities play a vital role in the global climate change mitigation agenda. City population density is one of the key factors that influence urban energy consumption and the subsequent GHG emissions. However, previous research on the relationship between population density and GHG emissions led to contradictory results due to urban/rural definition conundrum and the varying methodologies for estimating GHG emissions. This work addresses these ambiguities by employing the City Clustering Algorithm (CCA) and utilizing the gridded CO2 emissions data. Our results, derived from the analysis of all inhabited areas in the US, show a sublinear relationship between population density and the total emissions (i.e. the sum of onroad and building emissions) on a per capita basis. Accordingly, we find that doubling the population density would entail a reduction in the total CO2 emissions in buildings and onroad sectors typically by at least 42%. Moreover, we find that population density exerts a higher influence on onroad emissions than buildings emissions. From an energy consumption point of view, our results suggest that ongoing urban sprawl will lead to an increase in onroad energy consumption in cities and therefore stresses the importance of developing adequate local policy measures to limit urban sprawl. 
Energy Policy, (91)352–361  2016  
MaxJonathan Luckow, TF  On the computational complexity of length and neighborhoodconstrained path problems  IPL 156:105913 (2020)  arxiv  2018  
Anselmo García Cantú Ros, TF, and Jürgen P. Kropp  Variancebased control of regime shifts: bistability and oscillations  under review  arXiv  2014  
Philipp Zschoche, TF, Hendrik Molter, and Rolf Niedermeier  The Complexity of Finding Small Separators in Temporal Graphs
abstractTemporal graphs are graphs with timestamped edges. We study the problem of finding a small vertex set (the separator) with respect to two designated terminal vertices such that the removal of the set eliminates all temporal paths connecting one terminal to the other. Herein, we consider two models of temporal paths: paths that contain arbitrarily many edges per time step (non strict) and paths that contain at most one edge per time step (strict). Regarding the number of time steps of a temporal graph, we show a complexity dichotomy (NPhardness versus polynomial time solvability) for both problem variants. Moreover we prove both problem variants to be NPcomplete even on temporal graphs whose underlying graph is planar. We further show that, on temporal graphs with planar underlying graph, if additionally the number of time steps is constant, then the problem variant for strict paths is solvable in quasilinear time. Finally, we introduce and motivate the notion of a temporal core (vertices whose incident edges change over time). We prove that the nonstrict variant is fixedparameter tractable when parameterized by the size of the temporal core, while the strict variant remains NPcomplete, even for constantsize temporal cores. 
JCSS. 107: 7292 (2020)  MFCS 2018: 45:145:17 ✎  arXiv  2017 
Doctoral Thesis.
TF. Elements of Efficient Data Reduction: Fractals, Diminishers, Weights and Neighborhoods. Technische Universität Berlin.
Master Thesis.
TF. The Parameterized Complexity of Finding Paths with Shared Edges. Technische Universität Berlin.
Bachelor Thesis.
TF. Kritisches Verhalten eines verdünnten zufälligen Polymers. Technische Universität Berlin.
2019.
ALGO, September, 2019.  
ARDA, August, 2019. 
2018.
18th Workshop on Algorithmic Approaches for Transportation Modeling, Optimization, and Systems (ATMOS '18). In Helsinki, Finland, August 23 – 24 , 2018. 

Dagstuhl Seminar 18281: Synergies between Adaptive Analysis of Algorithms, Parameterized Complexity, Compressed Data Structures and Compressed Indices. In Dagstuhl, Germany, July 8 – 13 , 2018. 

44th International Workshop on GraphTheoretic Concepts in Computer Science (WG'18). In Lübbenau, Germany, June 2729, 2018. 

2017.
43rd International Workshop on GraphTheoretic Concepts in Computer Science (WG'17). In Eindhoven, The Netherlands, June 2123, 2017. 

2015.
Workshop on Kernelization (WORKER) 2015. In Nordfjordeid, Norway. June 2015. 

2019.
KolKom 2019, September, 2019.  
33rd AAAI Conference on Artificial Intelligence (AAAI '18) . Honolulu, Hawaii, USA, Jan 27Feb 01 

2018.
Conference on Computability in Europe (CiE'18) . Kiel, Germany, July 30August 3 

SIAM International Conference on Data Mining 2018 (SDM'18) . San Diego, CA, USA, May 35 

75. Workshop über Algorithmen und Komplexität . Universität Ulm, Ulm, Germany, April 1011 

2017.
74. Workshop über Algorithmen und Komplexität . Universität zu Lübeck, Lübeck, Germany, November 2324 

15th Algorithms and Data Structures Symposium (WADS) . St. John's, Newfundland, Canada, July 31August 2 

2016.
72. Workshop über Algorithmen und Komplexität . Leibniz Universität Hannover, Institut für Theoretische Informatik. 

11th International Symposium on Parameterized and Exact Computation (IPEC 2016) . In Aarhus, Denmark, August 2426. 

43rd International Colloquium on Automata, Languages and Programming (ICALP 2016). In Rome, Italy. July 2016. 

2015.
35th IARCS Annual Conference on Foundations of Software Technology and Theoretical Computer Science (FSTTCS 2015). In Bangalore, India. December 2015. 

7th Workshop on Graph Classes, Optimization, and Width Parameters (GROW). In Aussois, France. October 2015. Slides. 

The size distribution, scaling properties and spatial organization of urban clusters: a global and regional perspective. Verhandlungen der Deutschen Physikalischen Gesellschaft, AKE 14: Physics of Sustainability and HumanNature Interactions I, at Technische Universität Berlin, Germany. March 2015. 
2019.
Carsten Schubert. Preserving Paths in Temporal Graphs. Technische Universität Berlin, September, 2019. Bachelor thesis.
Dario Cavallaro. Hamiltonicity and the computational complexity of graph problems. Technische Universität Berlin, Juli, 2019. Bachelor thesis.
2018.
Leon Kellerhals. Parameterized Algorithms for Network Flows. Technische Universität Berlin, June 2018, Master thesis.
Valentin Rohm. Vertex Cover Under Time Constraints. Technische Universität Berlin, November 2018, Bachelor thesis.
2017.
Philipp Zschoche. On Finding Separators in Temporal Graphs. Technische Universität Berlin, August 2017, Master thesis.
MaxJonathan Luckow. Paths under Neighborhood Constraints — Algorithms and Complexity. Technische Universität Berlin, April 2017, Bachelor thesis.
2016.
Matthias Bentert. Parametrised Algorithms for Finding Triangles in Graphs  Detection, Counting and Enumeration. Technische Universität Berlin, December 2016, Master thesis.
Marco Morik. The Complexity of Routing with Collision Avoidance. Technische Universität Berlin, June, 2016. Bachelor thesis.
Maximilian Stahlberg. Finding the Most Vital Edges for Shortest Paths  Algorithms and Complexity for Special Graph Classes. Technische Universität Berlin, February, 2016. Bachelor thesis.
Analysis (and more) Scripts by Dirk Ferus (only in German).
Scripts by Wolfgang König (in the context of probabilistic theory, only in German): in particular, Wahrscheinlichkeitstheorie I+II and Stochastische Algorithmen.
Script by Stefan Felsner (together with students) about Graph theory (only in German)
Martin Aigner and Günter M. Ziegler: Proof from THE BOOK
TÜV Rheinland Berlin MarathonRelay
Orthodromic Spatial Clustering