1. Short technical note [Draft 3, 12 Sep 2013]
Detected communities in the relationship networks of PDAF releases from 2007-2009
Gabriel Sison, Pamela Anne Pasion and Giovanni Tapang*
National Institute of Physics, University of the Philippines Diliman
gtapang@nip.upd.edu.ph
Introduction
The release of the Commission on Audit (COA) Special Audits Office Report No. 2012-03 [1] opened a wealth of
data that gives us a glimpse of how the Priority Development Assistance Fund (PDAF) and the so-called Various
Infrastructure, Including Local Projects (VILP) funds were used in government agencies. The report gives a
government-wide Performance Audit of the PDAF and the VILP of various implementing agencies from 2007 to
2009.
The data is in several tables and annexes that listed the non-governmental organizations (NGO) to whom
funding was given by a legislator. One cannot immediately see the relationships between the NGOs and
between legislators by simply looking at the tables. Visualizing such relationships and quantifying them is a
problem of network analysis and visualization. The visualization of the PDAF relases is what we address in this
technical note.
We present a visualization at http://visser.ph/pdaf (using sigma.js).
Network Analysis
Network tools have been used in many applications to date. We have analyzed different systems such as prose
and poetry [2], SMS messages [3], translations [4], poetic styles [5] and bill co-authorships in the Philippine
Congress[6] among others.
A network is a simple way to represent a set of objects or nodes who has a defined relation between each other.
We call these objects a node or a vertex while we call the relationship between them as an edge [7]. Depending
on the data set, edges could represent different kinds of relationships. In a social network, these could be
friendship relations [8], or co-authorships in a Congressional setting[9]. Edges in networks can have values
attached to them (weighted networks) or one can set a uniform weight for the edges of an unweighted networks
[7]. Depending on the direction at which the relationship is defined we can have directed or undirected networks.
Networks have been used to characterized political systems such as the United States Congress [10] and the
Philippine House of Representatives[6]. In such networks, nodes (called ego or actors) are the legislators
themselves and links between them can be voting patterns or co-sponsorships of bills and resolutions[6] [or
common allocations to organizations such as in this paper]. These co-sponsorship networks can be used as
proxies for effective political party affiliation of the legislators which can be derived from calculating partitions, or
communities, that arise as a result of their level of partisanship[6,10].
In general, nodes or actors can be persons, groups or organizations. In the current work, we have our nodes as
the individual senators, congressmen, the implementing agencies (IA) and the NGOs that received their PDAF
allocations.
Methdology

2. We took data from Annex A of the COA special report[1] and converted this to a table using Python. We then
further processed the resulting data using an open source network analysis program Gephi[11]. We have taken
two visualizations of the data as presented by looking at the network built from legislators who have released
funds to a common NGO and another network where NGOs who received funds from the same legislator is
linked together. The two networks taken together defines a bipartite network but we only present visualization
and analysis of the two networks taken separately.
The first network that we created is a network where a legislator, represented by a node, is connected to another
legislator if the two of them provided funds to the same NGO. We applied community detection algorithms in
Gephi[11] to determine if there were groups of legislators that were likely to fund NGOs together. The legislators
were colored based on what “community” they fell into.
The same was done to make a network of NGOs to which the PDAF were transferred. In this network, a
connection is made between two NGOs if they received funds from the same legislator. As these nodes tend to
connect more with certain sets of nodes, we use the same community detection methods to find what NGOs are
connected with each other more and group them together by color. As with the legislator networks, the colors are
based on the communities that the NGOs fall into.
Results and Analysis
The legislator network that we obtained has 186 nodes with 3976 edges. We find that the network has an
average degree of 21.38. This implies that, on the average, a legislator distributes his PDAF to the same NGO
as 21.38 other congressmen as the degree is the number of connections that a node has. In this network, it
measures how many other legislators he or she shared the same beneficiary NGO. Certain legislators, based
on the COA report, have high degrees. For example, Rep. Adam Relson Jala tops the list at 73 with Arrel Olano
and Mariano Piamonte follow at 66 and 64 respectively. On the average, each legislator have given his PDAF to
1.4 NGOs.
The degree of a node does not reflect the amount that they gave from their PDAF since for Rep. Jala, he
allocated only P32.086 million for the period in review. This is just an average of P0.43 million per NGO while the
top releases belong to Senators Ramon Revilla (at P503.89 million for eight NGOs), Jinggoy Estrada (P491.495
million for four NGOs) and Juan Ponce Enrile (P469.49 million for 11 NGOs). In the legislator network, they have
degrees of 30, 24 and 49 respectively. To round up the top 5 in PDAF releases, Rep. Philip Pichay gave P180
million to two NGOs and Senator Angara gave P151 million to seven NGOs. Their degrees are 32 and 9
respectively.
We show in Figure 1 the legislator network and their detected communities. We found six (6) communities.
These communities are legislators that tend to give to the same set of NGOs together.
We can rank the legislators based the number of “partners” they have, of which Adam Relson Jala tops the list.
This is reflected in the high degree or number of connections that he has. We can also add weight to these links
by the number of times that two linked legislators funded a common NGO. In such a weighted network, Nerissa
Corazon-Ruiz becomes the most connected legislator. A more advanced technique is betweenness centrality
which measures how “central” a given node is, which can be seen as a proxy for influence within the network[10].
Sen. Juan Ponce Enrile tops that list. See Table 1 for more details.

3. We repeated the same thing for the NGO network. The NGO network shows an average degree of 7.68 which
implies that 7.68 NGOs typically receive allocations from the same legislator. The NGO network has 69 nodes
with 530 edges. We found at least five (5) groups of nodes or communities. Each groups are represented by a
separate node color. As such, nodes with the same color are NGOs which are more likely to have receive funds
from the same legislator.
Figure 1: Legislator network and detected
communities. Sizes of the nodes are proportional to the betweenness centrality.

4. In Figure 2, the thickness of the arrows represents the weight of how the NGOs are connected with each other.
The stronger the weight of the arrows, the greater their connection is. Thicker arrows are funded together more
often by more than one legislator. It can be seen that the nodes in blue and green groups have more weighted
edges than the other groups especially in the SDPFFI NGO in the blue group and KKAMFI NGO in the green
group.
We could also apply some other network techniques such as looking at which node are importante nodes in the
NGO network. This is measured by the eigenvector centrality[11]. The NGO which has the highest eigenvector
centrality or the measure of importance of the node is the MAMFI NGO. This is followed by the CARED and
SDPFF respectively with all of which have measure of greater than 0.90 eigenvector centrality. See Table 2 for
more numbers.
Node label Betweenness
Centrality
Weighted
Degree
Clustering
Coefficient
Eigenvector
Centrality
Closeness
Centrality
1 Juan Ponce Enrile 0.1034 140.0 0.2661 0.26504 0.5378
2 Arrel R. Olano 0.1024 87.0 0.3529 0.70891 0.5640
3 Ignacio T. Arroyo, Jr. 0.0750 95.0 0.5340 0.74083 0.5441
4 Adam Relson L. Jala 0.0650 110.0 0.3916 1.0 0.5763
5 Francisco T. Matugas 0.0649 78.0 0.4713 0.77308 0.5425
6 Edgardo J. Angara 0.0532 12.0 0.2778 0.03684 0.4048
7 Mariano U. Piamonte 0.0403 106.0 0.4638 0.95405 0.5378
8 Emmanuel Joel J.
Villanueva
0.0402 50.0 0.4366 0.43666 0.5082
9 Samuel M. Dangwa 0.0382 92.0 0.3286 0.26147 0.4973
10 Marc Douglas C. Cagas IV 0.0356 66.0 0.4473 0.46821 0.5211
Table 1. Various network parameters in the legislator network for the top-10 legislators based on their betweeness centrality.
Betweenness centrality is defined as the number of shortest paths from all vertices passing through a node. The weighted degree is
proportional to the number and strength of connections of a node. The clustering coefficient measures the degree to which nodes tend to
cluster with one another. The eignevector centrality is a measure of the importance of a node in the network and is related to how well
connected a particular node is. The closeness centrality is a measure of the average distance from a given strarting node to all other nodes
in the system. The centrality measures are normalized in this table.
These are just a sample of the things we can do with the network representation of the PDAF releases. Deeper
knowledge about Congress and the interlocking directorships of the NGOs would be also be extremely helpful in
further analysis. This will be done in a future work. Nevertheless these tools allow the average Filipino to glean
information readily as opposed to tables and documents, helping them better participate in the process of
democracy.

5. Node label Betweenness
Centrality
Weighted
Degree
Clustering
Coefficient
Eigenvector
Centrality
Closeness
Centrality
1 Kagandahan ng Kapaligiran
Foundation, Inc. (KKFI)
0.1982 21.0 0.3429 0.8683 0.5397
2 Kabuhayan at Kalusugan Alay sa
Masa Foundation, Inc. (KKAMFI)
0.1823 23.0 0.2332 0.6561 0.5574
3 Dr. Rodlofo A. Ignacio, Sr.
Foundation Inc (DRAISFI)
0.1275 23.0 0.3360 1.0 0.5667
4 Farmerbusiness Development Corp
(FDC)
0.0984 18.0 0.3399 0.7372 0.5231
5 Aaron Foundation Philippines Inc
(AFPI)
0.0915 15.0 0.2380 0.5067 0.4892
6 Masaganang Ani Para sa
Magsasaka Foundation Inc
(MAMFI)
0.0903 21.0 0.4048 0.8651 0.5397
7 Pangkabuhayan Foundation (Pang-
FI)
0.0838 21.0 0.3524 0.8509 0.4963
8 Kaagapay Magpakailanman
Foundation Inc (KMFI)
0.0681 17.0 0.3088 0.6091 0.5312
9 ITO NA Movement Foundation Inc
(ITO NA MI)
0.0571 11.0 0.3818 0.4734 0.4755
Figure 2: NGO networks and detected communities.

6. 10 Hand-Made Living Foundation Inc
(HMLFI)
0.0451 10.0 0.3556 0.4486 0.4626
Table 2. Various network parameters in the NGO network for the top-10 NGOs based on their betweeness centrality. Betweenness
centrality is defined as the number of shortest paths from all vertices passing through a node. The weighted degree is proportional to the
number and strength of connections of a node. The clustering coefficient measures the degree to which nodes tend to cluster with one
another. The eignevector centrality is a measure of the importance of a node in the network and is related to how well connected a particular
node is. The closeness centrality is a measure of the average distance from a given strarting node to all other nodes in the system. The
centrality measures are normalized in this table.
References
1. Commision on Audit Special Audits Office, Report No. 2012-03 Government-wide Performance Audit, “Priority
Development Assistance Fund (PDAF) and Various Infrastructures including Local Projects (VILP)”, 2012
2. RM Roxas, G Tapang, “Prose and Poetry Classification and Boundary Detection Using Word Adjacency
Network Analysis”, International Journal of Modern Physics C 21 (04), 503-512
3. JJT Cabatbat, GA Tapang, “Texting Styles and Information Change of SMS Text Messages in Filipino”,
International Journal of Modern Physics C 24 (02)
4. JJT Cabatbat, JP Monsanto, GA Tapang, “Preserved Network Metrics Across Translated Texts,” International
Journal of Modern Physics C (accepted paper 2013)
5. RM Roxas-Villanueva, MK Nambatac, G Tapang, “Characterizing English poetic style using complex
networks”, International Journal of Modern Physics C 23 (02)
6. Gabriel Dominik Sison, “Edge-weight distributions in dense small node co-authorship networks“, BS Thesis,
BS Physics, UP Diliman April 2013
7. Alain Barrat, Marc Barth elemy, and Alessandro Vespignani. Dynamical Processes on Complex Cambridge
University Press, New York, USA, 2008.
8. Hua Wang and Barry Wellman. Social connectivity in america: changes in adult friendship network size from
2002 to 2007. American Behavioral Scientist, 53(8):1148{1169, 2010.
9. James H Fowler. Connecting the congress: A study of cosponsorship networks. Political Analysis, 14(4):456-
487, 2006.
10. Yan Zhang, AJ Friend, Amanda L Traud, Mason A Porter, James H Fowler, and Peter J Mucha. Community
structure in congressional cosponsorship networks. Physica A: Statistical Mechanics and its Applications,
387(7):1705{1712, 2008.
11. Bastian M., Heymann S., Jacomy M. (2009). Gephi: an open source software for exploring and manipulating
networks. International AAAI Conference on Weblogs and Social Media.
About the authors
Pamela Anne Pasion is a fourth-year BS Applied Physics student at the National Institute of Physics working on
translations and network analysis. Gabriel Dominik Sison has finished his BS Physics degree in 2013 with an
award for Best BS Thesis on his work “Edge-weight distributions in dense small node co-authorship networks“.
Dr. Giovanni Tapang is an Associate Professor at the National Institute of Physics and is also the chairperson
of the scientist group AGHAM-Advocates of Science and Technology for the People. He can be reached at
gtapang@nip.upd.edu.ph