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Frank Havemann

Publications and source records attributed to Frank Havemann.

11 recordsLinked to original sources
arXiv2020

Topics as Clusters of Citation Links to Highly Cited Sources: The Case of Research on International Relations

Following Henry Small in his approach to co-citation analysis, highly cited sources are seen as concept symbols of research fronts. But instead of co-cited sources I cluster citation links, which are the thematically least heterogenous elements in bibliometric studies. To obtain clusters representing topics characterised by concepts I restrict link clustering to citation links to highly cited sources. Clusters of citation links between papers in a political-science subfield (International Relations) and 300 of their sources most cited in the period 2006-2015 are constructed by a local memetic algorithm. It finds local minima in a cost landscape corresponding to clusters, which can overlap each other pervasively. The clusters obtained are well separated from the rest of the network but can have suboptimal cohesion. Cohesive cores of topics are found by applying an algorithm that constructs core-periphery structures in link sets. In this methodological paper I only discuss some first clustering results for the second half of the 10-years period.

cs.DL↗
arXiv2018

Communities as Well Separated Subgraphs With Cohesive Cores: Identification of Core-Periphery Structures in Link Communities

Communities in networks are commonly considered as highly cohesive subgraphs which are well separated from the rest of the network. However, cohesion and separation often cannot be maximized at the same time, which is why a compromise is sought by some methods. When a compromise is not suitable for the problem to be solved it might be advantageous to separate the two criteria. In this paper, we explore such an approach by defining communities as well separated subgraphs which can have one or more cohesive cores surrounded by peripheries. We apply this idea to link communities and present an algorithm for constructing hierarchical core-periphery structures in link communities and first test results.

cs.SI↗
arXiv2017

Memetic search for overlapping topics based on a local evaluation of link communities

In spite of recent advances in field delineation methods, bibliometricians still don't know the extent to which their topic detection algorithms reconstruct `ground truths', i.e. thematic structures in the scientific literature. In this paper, we demonstrate a new approach to the delineation of thematic structures that attempts to match the algorithm to theoretically derived and empirically observed properties all thematic structures have in common. We cluster citation links rather than publication nodes, use predominantly local information and search for communities of links starting from seed subgraphs in order to allow for pervasive overlaps of topics. We evaluate sets of links with a new cost function and assume that local minima in the cost landscape correspond to link communities. Because this cost landscape has many local minima we define a valid community as the community with the lowest minimum within a certain range. Since finding all valid communities is impossible for large networks, we designed a memetic algorithm that combines probabilistic evolutionary strategies with deterministic local searches. We apply our approach to a network of about 15,000 Astronomy & Astrophysics papers published 2010 and their cited sources, and to a network of about 100,000 Astronomy & Astrophysics papers (published 2003--2010) which are linked through direct citations.

cs.SI↗
arXiv2015

Detecting Overlapping Link Communities by Finding Local Minima of a Cost Function with a Memetic Algorithm. Part 1: Problem and Method

We propose an algorithm for detecting communities of links in networks which uses local information, is based on a new evaluation function, and allows for pervasive overlaps of communities. The complexity of the clustering task requires the application of a memetic algorithm that combines probabilistic evolutionary strategies with deterministic local searches. In Part 2 we will present results of experiments with with citation networks.

cs.SI↗
arXiv2014

Bibliometric Indicators of Young Authors in Astrophysics: Can Later Stars be Predicted?

We test 16 bibliometric indicators with respect to their validity at the level of the individual researcher by estimating their power to predict later successful researchers. We compare the indicators of a sample of astrophysics researchers who later co-authored highly cited papers before their first landmark paper with the distributions of these indicators over a random control group of young authors in astronomy and astrophysics. We find that field and citation-window normalisation substantially improves the predicting power of citation indicators. The two indicators of total influence based on citation numbers normalised with expected citation numbers are the only indicators which show differences between later stars and random authors significant on a 1% level. Indicators of paper output are not very useful to predict later stars. The famous $h$-index makes no difference at all between later stars and the random control group.

cs.DL↗
arXiv2013

Evaluating Overlapping Communities with the Conductance of their Boundary Nodes

Usually the boundary of a community in a network is drawn between nodes and thus crosses its outgoing links. If we construct overlapping communities by applying the link-clustering approach nodes and links interchange their roles. Therefore, boundaries must drawn through the nodes shared by two or more communities. For the purpose of community evaluation we define a conductance of boundary nodes of overlapping communities analogously to the graph conductance of boundary-crossing links used to partition a graph into disjoint communities. We show that conductance of boundary nodes (or normalised node cut) can be deduced from ordinary graph conductance of disjoint clusters in the network's weighted line graph introduced by Evans and Lambiotte (2009) to get overlapping communities of nodes in the original network. We test whether our definition can be used to construct meaningful overlapping communities with a local greedy algorithm of link clustering. In this note we present encouraging results we obtained for Zachary's karate-club network.

cs.SI↗
arXiv2012

Bibliometric Networks

This text is based on a translation of a chapter in a handbook about network analysis (published in German) where we tried to make beginners familiar with some basic notions and recent developments of network analysis applied to bibliometric issues (Havemann and Scharnhorst 2010). We have added some recent references.

cs.DL↗
arXiv2011

Identifying Overlapping and Hierarchical Thematic Structures in Networks of Scholarly Papers: A Comparison of Three Approaches

We implemented three recently proposed approaches to the identification of overlapping and hierarchical substructures in graphs and applied the corresponding algorithms to a network of 492 information-science papers coupled via their cited sources. The thematic substructures obtained and overlaps produced by the three hierarchical cluster algorithms were compared to a content-based categorisation, which we based on the interpretation of titles and keywords. We defined sets of papers dealing with three topics located on different levels of aggregation: h-index, webometrics, and bibliometrics. We identified these topics with branches in the dendrograms produced by the three cluster algorithms and compared the overlapping topics they detected with one another and with the three pre-defined paper sets. We discuss the advantages and drawbacks of applying the three approaches to paper networks in research fields.

physics.soc-ph↗
arXiv2010

Identification of overlapping communities and their hierarchy by locally calculating community-changing resolution levels

We propose a new local, deterministic and parameter-free algorithm that detects fuzzy and crisp overlapping communities in a weighted network and simultaneously reveals their hierarchy. Using a local fitness function, the algorithm greedily expands natural communities of seeds until the whole graph is covered. The hierarchy of communities is obtained analytically by calculating resolution levels at which communities grow rather than numerically by testing different resolution levels. This analytic procedure is not only more exact than its numerical alternatives such as LFM and GCE but also much faster. Critical resolution levels can be identified by searching for intervals in which large changes of the resolution do not lead to growth of communities. We tested our algorithm on benchmark graphs and on a network of 492 papers in information science. Combined with a specific post-processing, the algorithm gives much more precise results on LFR benchmarks with high overlap compared to other algorithms and performs very similar to GCE.

physics.data-an↗
arXiv2010

Identification of Overlapping Communities by Locally Calculating Community-Changing Resolution Levels

An algorithm for the detection of overlapping natural communities in networks was proposed by Lancichinetti, Fortunato, and Kertesz (LFK) last year. The LFK algorithm constructs natural communities of (in principle) all nodes of a graph by maximising the local fitness of communities. The resulting modules can overlap. The generation of communities can easily be repeated for many values of resolution; thus allowing different views on the network at different resolutions. We implemented the main idea of the LFK algorithm---to generate natural communities of each node of a network---in a different way. We start with a value of the resolution parameter that is high enough for each node to be its own natural community. As soon as the resolution is reduced, each node acquires other nodes as members of its community, i.e. natural communities grow. For each community found at a certain resolution level we calculate the next lower resolution where a node is added. After adding a node to a community of a seed node we check whether it is also the natural community of a node that we have already analysed. In this case, we can stop expanding the seed node's community. We tested our algorithm on a small benchmark graph and on a network of about 500 papers in information science (weighted with the Salton index of bibliographic coupling). In our tests, this approach results in characteristic ranges of resolution where a large resolution change does not lead to a growth of the natural community. Such stable modules were also obtained by applying the LFK algorithm but since we determine communities for all resolution values in one run, our approach is faster than the LFK reference. And our algorithm reveals the hierarchical structure of the graph more easily.

physics.data-an↗