485,719 research outputs found
Estimating the resolution limit of the map equation in community detection
A community detection algorithm is considered to have a resolution limit if
the scale of the smallest modules that can be resolved depends on the size of
the analyzed subnetwork. The resolution limit is known to prevent some
community detection algorithms from accurately identifying the modular
structure of a network. In fact, any global objective function for measuring
the quality of a two-level assignment of nodes into modules must have some sort
of resolution limit or an external resolution parameter. However, it is yet
unknown how the resolution limit affects the so-called map equation, which is
known to be an efficient objective function for community detection. We derive
an analytical estimate and conclude that the resolution limit of the map
equation is set by the total number of links between modules instead of the
total number of links in the full network as for modularity. This mechanism
makes the resolution limit much less restrictive for the map equation than for
modularity, and in practice orders of magnitudes smaller. Furthermore, we argue
that the effect of the resolution limit often results from shoehorning
multi-level modular structures into two-level descriptions. As we show, the
hierarchical map equation effectively eliminates the resolution limit for
networks with nested multi-level modular structures.Comment: 12 pages, 7 figure
Detecting Stable Communities In Large Scale Networks
A network is said to exhibit community structure if the nodes of the network can be easily grouped into groups of nodes, such that each group is densely connected internally but sparsely connected with other groups. Most real world networks exhibit community structure.
A popular technique for detecting communities is based on computing the modularity of the network. Modularity reflects how well the vertices in a group are connected as opposed to being randomly connected. We propose a parallel algorithm for detecting modularity in large networks.
However, all modularity based algorithms for detecting community structure are affected by the order in which the vertices in the network are processed. Therefore, detecting communities in real world graphs becomes increasingly difficult. We introduce the concept of stable community, that is, a group of vertices that are always partitioned to the same community independent of the vertex perturbations to the input. We develop a preprocessing step that identifies stable communities and empirically show that the number of stable communities in a network affects the range of modularity values obtained. In particular, stable communities can also help determine strong communities in the network.
Modularity is a widely accepted metric for measuring the quality of a partition identified by various community detection algorithms. However, a growing number of researchers have started to explore the limitations of modularity maximization such as resolution limit, degeneracy of solutions and asymptotic growth of the modularity value for detecting communities. In order to address these issues we propose a novel vertex-level metric called permanence. We show that our metric permanence as compared to other standard metrics such as modularity, conductance and cut-ratio performs as a better community scoring function for evaluating the detected community structures from both synthetic networks and real-world networks. We demonstarte that maximizing permanence results in communities that match the ground-truth structure of networks more accurately than modularity based and other approaches. Finally, we demonstrate how maximizing permanence overcomes limitations associated with modularity maximization
Extension of Modularity Density for Overlapping Community Structure
Modularity is widely used to effectively measure the strength of the disjoint
community structure found by community detection algorithms. Although several
overlapping extensions of modularity were proposed to measure the quality of
overlapping community structure, there is lack of systematic comparison of
different extensions. To fill this gap, we overview overlapping extensions of
modularity to select the best. In addition, we extend the Modularity Density
metric to enable its usage for overlapping communities. The experimental
results on four real networks using overlapping extensions of modularity,
overlapping modularity density, and six other community quality metrics show
that the best results are obtained when the product of the belonging
coefficients of two nodes is used as the belonging function. Moreover, our
experiments indicate that overlapping modularity density is a better measure of
the quality of overlapping community structure than other metrics considered.Comment: 8 pages in Advances in Social Networks Analysis and Mining (ASONAM),
2014 IEEE/ACM International Conference o
Measuring Youth Program Quality: A Guide to Assessment Tools
Thanks to growing interest in the subject of youth program quality, many tools are now available to help organizations and systems assess and improve quality. Given the size and diversity of the youth-serving sector, it is unrealistic to expect that any one tool or process will fit all programs or circumstances. This report compares the purpose, history, structure, methodology, content and technical properties of nine different program observation tools
VALUING COMMUNITY DEVELOPMENT THROUGH THE SOCIAL INCLUSION PROGRAMME (SICAP) 2015–2017 TOWARDS A FRAMEWORK FOR EVALUATION. ESRI RESEARCH SERIES NUMBER 77 FEBRUARY 2019
The Social Inclusion and Community Activation Programme (SICAP) represents a
major component of Ireland’s community development strategy, led by the
Department of Rural and Community Development (DRCD). The vision of SICAP is
to improve the opportunities and life chances of those who are marginalised in
society, experiencing unemployment or living in poverty through community
development approaches, targeted supports and interagency collaboration, where
the values of equality and inclusion are promoted and human rights are respected.
In 2016, total expenditure on SICAP amounted to approximately €36 million (Pobal,
2016a).
Using a mixed methodology, this report examines the extent to which community
development programmes can or should be subject to evaluation, with a particular
focus on SICAP. In doing so, the report draws on a rich body of information –
including desk-based research; consultation workshops with members of local
community groups (LCGs), local community workers (LCWs) and other key policy
stakeholders; and an analysis of administrative data held by Pobal – on the
characteristics of LCGs that received direct support under SICAP. The findings in
this report relate to the delivery of the SICAP 2015–2017 programme which ended
in December 2017.
The aim of the study is to inform policy by shedding light on a number of issues
including the following.
Can community development be evaluated?
What are the current metrics and methodologies suggested in the literature for
evaluating community development interventions?
What possible metrics can be used to evaluate community development
interventions and how do these relate to the SICAP programme?
How can a framework be developed that could potentially be used by SICAP for
monitoring evaluation of its community development programme
Comparison and validation of community structures in complex networks
The issue of partitioning a network into communities has attracted a great
deal of attention recently. Most authors seem to equate this issue with the one
of finding the maximum value of the modularity, as defined by Newman. Since the
problem formulated this way is NP-hard, most effort has gone into the
construction of search algorithms, and less to the question of other measures
of community structures, similarities between various partitionings and the
validation with respect to external information. Here we concentrate on a class
of computer generated networks and on three well-studied real networks which
constitute a bench-mark for network studies; the karate club, the US college
football teams and a gene network of yeast. We utilize some standard ways of
clustering data (originally not designed for finding community structures in
networks) and show that these classical methods sometimes outperform the newer
ones. We discuss various measures of the strength of the modular structure, and
show by examples features and drawbacks. Further, we compare different
partitions by applying some graph-theoretic concepts of distance, which
indicate that one of the quality measures of the degree of modularity
corresponds quite well with the distance from the true partition. Finally, we
introduce a way to validate the partitionings with respect to external data
when the nodes are classified but the network structure is unknown. This is
here possible since we know everything of the computer generated networks, as
well as the historical answer to how the karate club and the football teams are
partitioned in reality. The partitioning of the gene network is validated by
use of the Gene Ontology database, where we show that a community in general
corresponds to a biological process.Comment: To appear in Physica A; 25 page
Identifying the underlying structure and dynamic interactions in a voting network
We analyse the structure and behaviour of a specific voting network using a
dynamic structure-based methodology which draws on Q-Analysis and social
network theory. Our empirical focus is on the Eurovision Song Contest over a
period of 20 years. For a multicultural contest of this kind, one of the key
questions is how the quality of a song is judged and how voting groups emerge.
We investigate structures that may identify the winner based purely on the
topology of the network. This provides a basic framework to identify what the
characteristics associated with becoming a winner are, and may help to
establish a homogenous criterion for subjective measures such as quality.
Further, we measure the importance of voting cliques, and present a dynamic
model based on a changing multidimensional measure of connectivity in order to
reveal the formation of emerging community structure within the contest.
Finally, we study the dynamic behaviour exhibited by the network in order to
understand the clustering of voting preferences and the relationship between
local and global properties.Comment: 20 pages, 10 figures, 3 tables, submitted to Physica
- …