Fiona Skerman
Uppsala University
I am researcher (biträdande lektor) at Uppsala University. My previous position was a Heilbronn Research Fellow at Bristol University. Before this I was a postdoc with Dan Kráľ at Masaryk University, with Cecilia Holmgren at Uppsala University and with the Heilbronn Institute at Bristol University. My DPhil with Colin McDiarmid researched the modularity of networks and for which I was awarded the Corcoran Memorial Prize for the best thesis in the Oxford Statistics Department [thesis]. Before coming to Oxford, I did my honours thesis with Brendan McKay at the Australian National University, looking at likely degree sequences in random bipartite graphs [thesis].
ALEA Young Workshop Normandy 2019
Here are the lecture notes and the exercises for course in Random Graphs, Thresholds and Phase transitions.
Preprints
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No additional tournaments are quasirandomforcing with Robert Hancock, Adam Kabela, Dan Kráľ, Taísa Martins, Roberto Parente and Jan Volec; Submitted. [arXiv] [pdf] [slides]
A tournament $H$ is quasirandomforcing if the following holds for every sequence $(G_n)_{n}$ of tournaments of growing orders: if the density of $H$ in $G_n$ converges to the expected density of $H$ in a random tournament, then $(G_n)_n$ is quasirandom. Every transitive tournament with at least 4 vertices is quasirandomforcing (link), and Coregliano et al. showed that there is also a nontransitive 5vertex tournament with the property (link). We show that no additional tournament has this property.
Assigning times to minimise reachability in temporal graphs with Kitty Meeks and Jess Enright; Submitted. [arXiv] [pdf]
Temporal graphs, in which edges are active at specified times, are of relevance for spreading processes on graphs. In our setting the number of active times for each edge is fixed, but we can change the relative order in which (sets of) edges are active. We investigate the problem of determining an ordering of edges that minimises the maximum number of vertices reachable from any single starting vertex. Epidemiologically, this corresponds to the worstcase number of vertices infected in a single disease outbreak.
Publications and accepted manuscripts
Do not hesitate to email if you have trouble accessing any of the manuscripts listed below.
The parameterised complexity of computing the maximum modularity of a graph with Kitty Meeks; Algorithmica (2019) + IPEC (2018). [journal] [IPEC] [arXiv] [pdf]
Determining the maximum modularity of a graph is known to be NPcomplete in general, even to find a multiplicative approximation, but we show it has reduced complexity with respect to some structural parameterisations of the input graph.
On the other hand we prove modularity is W[1]hard (and hence unlikely to admit an FPT algorithm) when parameterised simultaneously by pathwidth and the size of a minimum feedback vertex set.
Permutations in binary trees and split trees with Michael Albert, Cecilia Holmgren and Tony Johansson; Algorithmica (2020). [journal] [arXiv] [pdf] [slides]
Given a tree on $n$ vertices, we randomly label the vertices 1 to $n$. An occurence of a length $k$ permutation $\sigma$ is a sequence of vertices on a common descending path in the tree whose labels, when read from root to leaf, are ordered according to $\sigma$. We calculate the number of occurences of fixed length permutations in binary trees and split trees.
For the trees considered, this generalizes the inversion results below from $\sigma=21$ to more general permutations $\sigma$.
Conference version: AofA
Modularity of ErdosRenyi random graphs with Colin McDiarmid; Random Structures and Algorithms: published online
[journal] [arXiv] [pdf]
Modularity introduced by Newman and Girvan (in this paper) is a popular metric in community detection.
Two key features which we find for ErdosRenyi random graphs are that the modularity is $1+o(1)$ with high probability (whp) for $np$ up to $1+o(1)$ (and no further); and when $np\geq 1$ and $p$ is bounded below 1, it has order $(np)^{1/2}$ whp, in accord with a conjecture by Reichardt and Bornholdt in 2006 (and disproving another conjecture from the physics literature).
Conference version: AofA
Random tree recursions: which fixed points correspond to tangible sets of trees? with Toby Johnson and Moumanti Podder; Random Structures and Algorithms 56 3 (2020) [journal] [arXiv] [pdf]
We answer the following question of Joel Spencer:
Say that a set of trees $\mathcal{B}$ follows the atleasttwo rule if $t \in \mathcal{B}$
if and only if the root of $t$ has two children $u$ and $v$ such that the subtrees rooted at $u$ and $v$ are also in $\mathcal{B}$. Suppose that $\lambda > \lambda_{crit}$. Does there exist a set of trees $\mathcal{B}$ with this metaproperty such that for random tree $T_\lambda$, we have that $\mathbb{P}[T_\lambda \in \mathcal{B}]$ is the middle solution of the classical fixed point equation?
$k$ cuts on paths and some trees with Xing Shi Cai, Cecilia Holmgren and Luc Devroye; Electronic Journal of Probability 24 (2019) [journal] [arXiv] [pdf]
We define a new cutting process on trees the `blunt scissors' or $k$cut model where each node must be cut $k$ times before it is destroyed. For $k=1$ this is the model of Meir and Moon. This paper studies the distribution of the $k$cut number of a path on $n$ vertices.
Inversions in split trees and GaltonWatson trees with Xing Shi Cai, Cecilia Holmgren, Svante Janson and Tony Johansson. Combinatorics, Probability and Computing 28:3 (2019). [journal] [arXiv] [pdf]
We study $I(T)$, the number of inversions in a tree $T$ with its vertices labeled uniformly
at random, which is a generalization of inversions in permutations. We first show that the
cumulants of $I(T)$ have explicit formulas involving the $k$total common ancestors of $T$ (an
extension of the total path length). For three sequence of trees $T_n$ we consider $X_n$ the normalized version of $I(T_n)$.
We identify the limit of $X_n$ for complete $b$ary trees. For
$T_n$ being split trees, we show that $X_n$ converges to the unique solution of a distributional
equation and when $T_n$’s are conditional Galton–Watson trees, we show that $X_n$ converges
to a random variable defined in terms of Brownian excursions. By exploiting the connection
between inversions and the total path length, we extend earlier results by Panholzer and Seitz.
Conference version: [AofA]
Modularity of regular and treelike graphs with Colin McDiarmid; Oxford Journal of Complex Networks (6) 4 (2018) [journal] [arXiv] [pdf]
Modularity introduced by Newman and Girvan (in this paper) is a popular metric in community detection.
We show that random cubic graphs usually have modularity in the interval (0.666, 0.804); and random $r$regular graphs for large $r$ usually have modularity $\Theta(1/\sqrt{r})$. Our results give thresholds for the statistical significance of clustering found in large regular networks.
The modularity of cycles and low degree trees is known to be asymptotically 1. We extend these results to all graphs whose product of treewidth and maximum degree is much less than the number of edges. This shows for example that random planar graphs typically have modularity close to 1.
Guessing Numbers of Odd Cycles with Ross Atkins and Puck Rombach; Electronic Journal of Combinatorics 24:1 (2017) [journal] [arXiv] [pdf]
For a given number of colours, $s$, the guessing number of a graph is the base $s$ logarithm
of the size of the largest family of colourings of the vertex set of the graph such that the colour
of each vertex can be determined from the colours of the vertices in its neighbourhood. We show that, for any given
integer $s \geq 2$, if $a$ is the largest factor of $s$ less than or equal to $\sqrt{s}$, for sufficiently large odd $n$,
the guessing number of the cycle $C_n$ with $s$ colours is $(n 1)/2 + \log_s (a)$. This answers a question posed in this paper by
Christofides and Markstrom in 2011.
Degree sequences of random digraphs and bipartite graphs with Brendan McKay; Journal of Combinatorics 7:1 (2016) [journal] [arXiv] [pdf]
We investigate the joint distribution of the vertex degrees in three models of random bipartite graphs. Namely, we can choose each edge with a specified probability, choose a specified number of edges, or specify the vertex degrees in one of the two colour classes.
In each case, provided the two colour classes are not too different in size nor the number of edges too low, we define a probability space based on independent binomial variables and show that its probability masses asymptotically equal those of the degrees in the graph model almost everywhere. The accuracy is sufficient to asymptotically determine the expectation of any joint function of the degrees whose maximum is at most polynomially greater than its expectation.
AvoiderEnforcer Star Games with Andrzej Grzesik, Mirjana Mikalački, Zoltán Nagy, Alon Naor, Balázs Patkós; Discrete Mathematics and Theoretical Computer Science 17:1 (2015) [journal] [arXiv] [pdf]
In this paper, we study $(1 : b)$ AvoiderEnforcer games played on the edge set of the complete graph on n vertices.
For every constant $k \geq 3$ we analyse the $k$star game, where Avoider tries to avoid claiming $k$ edges incident to
the same vertex. We consider both versions of AvoiderEnforcer games  the strict and the monotone  and for each
provide explicit winning strategies for both players. We determine the order of magnitude of the threshold biases
$f_\mathcal{F}^{mon}$, $f_\mathcal{F}^{}$ and $f_\mathcal{F}^{+}$, where $\mathcal{F}$ is the hypergraph of the game.
Modularity in random regular graphs and lattices with Colin McDiarmid; Electronic Notes in Discrete Mathematics 43 (2013) [journal]
Given a graph $G$, the modularity of a partition of the vertex set measures the extent to which edge density is higher within parts than between parts; and the modularity of $G$ is the maximum modularity of a partition.
We give an upper bound on the modularity of rregular graphs as a function of the edge expansion (or isoperimetric number) under the restriction that each part in our partition has a sublinear numbers of vertices. This leads to results for random $r$regular graphs. In particular we show the modularity of a random cubic graph partitioned into sublinear parts is almost surely in the interval $(0.66, 0.88)$.
