Dissertation: Mason's remarks

dissertation/temporalgraphs.bib

@@ -386,4 +386,60 @@
 	date = {2006-05-01},
 	langid = {english},
 	file = {Eagle and Pentland - 2006 - Reality mining sensing complex social systems.pdf:/home/dimitri/Zotero/storage/H9DUQJ6T/Eagle and Pentland - 2006 - Reality mining sensing complex social systems.pdf:application/pdf;Snapshot:/home/dimitri/Zotero/storage/8DH79ULJ/10.html:text/html}
+}
+
+@article{holme_temporal_2012,
+	title = {Temporal networks},
+	volume = {519},
+	issn = {0370-1573},
+	url = {http://www.sciencedirect.com/science/article/pii/S0370157312000841},
+	doi = {10.1016/j.physrep.2012.03.001},
+	series = {Temporal Networks},
+	abstract = {A great variety of systems in nature, society and technology–from the web of sexual contacts to the Internet, from the nervous system to power grids–can be modeled as graphs of vertices coupled by edges. The network structure, describing how the graph is wired, helps us understand, predict and optimize the behavior of dynamical systems. In many cases, however, the edges are not continuously active. As an example, in networks of communication via e-mail, text messages, or phone calls, edges represent sequences of instantaneous or practically instantaneous contacts. In some cases, edges are active for non-negligible periods of time: e.g., the proximity patterns of inpatients at hospitals can be represented by a graph where an edge between two individuals is on throughout the time they are at the same ward. Like network topology, the temporal structure of edge activations can affect dynamics of systems interacting through the network, from disease contagion on the network of patients to information diffusion over an e-mail network. In this review, we present the emergent field of temporal networks, and discuss methods for analyzing topological and temporal structure and models for elucidating their relation to the behavior of dynamical systems. In the light of traditional network theory, one can see this framework as moving the information of when things happen from the dynamical system on the network, to the network itself. Since fundamental properties, such as the transitivity of edges, do not necessarily hold in temporal networks, many of these methods need to be quite different from those for static networks. The study of temporal networks is very interdisciplinary in nature. Reflecting this, even the object of study has many names—temporal graphs, evolving graphs, time-varying graphs, time-aggregated graphs, time-stamped graphs, dynamic networks, dynamic graphs, dynamical graphs, and so on. This review covers different fields where temporal graphs are considered, but does not attempt to unify related terminology—rather, we want to make papers readable across disciplines.},
+	pages = {97--125},
+	number = {3},
+	journaltitle = {Physics Reports},
+	shortjournal = {Physics Reports},
+	author = {Holme, Petter and Saramäki, Jari},
+	urldate = {2018-07-31},
+	date = {2012-10-01},
+	file = {ScienceDirect Snapshot:/home/dimitri/Zotero/storage/KUU88J97/S0370157312000841.html:text/html}
+}
+
+@article{holme_modern_2015,
+	title = {Modern temporal network theory: a colloquium},
+	volume = {88},
+	issn = {1434-6028, 1434-6036},
+	url = {https://link.springer.com/article/10.1140/epjb/e2015-60657-4},
+	doi = {10.1140/epjb/e2015-60657-4},
+	shorttitle = {Modern temporal network theory},
+	abstract = {The power of any kind of network approach lies in the ability to simplify a complex system so that one can better understand its function as a whole. Sometimes it is beneficial, however, to include more information than in a simple graph of only nodes and links. Adding information about times of interactions can make predictions and mechanistic understanding more accurate. The drawback, however, is that there are not so many methods available, partly because temporal networks is a relatively young field, partly because it is more difficult to develop such methods compared to for static networks. In this colloquium, we review the methods to analyze and model temporal networks and processes taking place on them, focusing mainly on the last three years. This includes the spreading of infectious disease, opinions, rumors, in social networks; information packets in computer networks; various types of signaling in biology, and more. We also discuss future directions.},
+	pages = {234},
+	number = {9},
+	journaltitle = {The European Physical Journal B},
+	shortjournal = {Eur. Phys. J. B},
+	author = {Holme, Petter},
+	urldate = {2018-07-31},
+	date = {2015-09-01},
+	langid = {english},
+	file = {Snapshot:/home/dimitri/Zotero/storage/CYSLT5MA/10.html:text/html}
+}
+
+@article{tomita_worst-case_2006,
+	title = {The worst-case time complexity for generating all maximal cliques and computational experiments},
+	volume = {363},
+	issn = {0304-3975},
+	url = {http://www.sciencedirect.com/science/article/pii/S0304397506003586},
+	doi = {10.1016/j.tcs.2006.06.015},
+	series = {Computing and Combinatorics},
+	abstract = {We present a depth-first search algorithm for generating all maximal cliques of an undirected graph, in which pruning methods are employed as in the Bron–Kerbosch algorithm. All the maximal cliques generated are output in a tree-like form. Subsequently, we prove that its worst-case time complexity is O(3n/3) for an n-vertex graph. This is optimal as a function of n, since there exist up to 3n/3 maximal cliques in an n-vertex graph. The algorithm is also demonstrated to run very fast in practice by computational experiments.},
+	pages = {28--42},
+	number = {1},
+	journaltitle = {Theoretical Computer Science},
+	shortjournal = {Theoretical Computer Science},
+	author = {Tomita, Etsuji and Tanaka, Akira and Takahashi, Haruhisa},
+	urldate = {2018-07-31},
+	date = {2006-10-25},
+	keywords = {Computational experiments, Enumeration, Maximal cliques, Worst-case time complexity},
+	file = {ScienceDirect Full Text PDF:/home/dimitri/Zotero/storage/QDLTAXHX/Tomita et al. - 2006 - The worst-case time complexity for generating all .pdf:application/pdf;ScienceDirect Snapshot:/home/dimitri/Zotero/storage/TCJ8J7MV/S0304397506003586.html:text/html}
 }