detected communities and structure in the ngo co-funding...

Key words: complex systems, 89.75.-k, social systems 89.65.-s

Detected Communities and Structure in the NGO Co-funding Networks of PDAF Releases from 2007-2009

*Corresponding author: [email protected]

Gabriel Dominik Sison*, Pamela Anne Pasion, and Giovanni Alarkon Tapang

Using network theory, the researchers visualize and analyze relationships that can be found in the Priority Development Assistance Fund (PDAF) allocation from the released 2012 report of the Commission of Audit (COA). Strong community structure was seen in the legislator-legislator co-funding network and NGO-NGO co-funding network as indicated by the high values of modularity, 0.5 and 0.4 respectively. Also, communities in the legislator-legislator network do not correspond to parties but they do try to incorporate members of the ruling party.

Philippine Journal of Science147 (3): 383-392, September 2018ISSN 0031 - 7683Date Received: 03 Nov 2017

National Institute of Physics, University of the Philippines Diliman, Quezon City, Philippines

INTRODUCTIONVarious systems in nature are driven by mechanisms with non-trivial interactions. From language (Roxas & Tapang 2010) to politics (Zhang et al. 2008), these systems are highly complex with behaviors that are hard to predict. However, these systems are still of interest to us, so their analysis has driven the development of new methods. One possible method is to study the small-scale interactions between the individual elements along with their patterns and structure. The researchers want to see if these patterns and structures reflect real properties of the system. This is the basis of using network science to analyze systems.

A network is a simple way to represent a set of objects or nodes that have relationships with each other. These objects are called nodes or vertices, while call the relationship between them is called an edge (Barrat et al. 2008). Depending on the data set, edges could represent different kinds of relationships. In a social network, these could be friendship relations (Wang & Wellman 2010) or co-authorships in a congressional setting (Fowler 2006). Edges in networks can have values attached to them

(weighted networks) or be set to have a uniform weight of one for unweighted networks (Barrat et al. 2008).

Network tools have been used in many applications to date. The researchers have analyzed different systems such as prose and poetry (Roxas & Tapang 2010), SMS messages (Cabatbat & Tapang 2013), translations (Cabatbat et al. 2014), poetic styles (Roxas-Villanueva et al. 2012) and bill co-authorships in the Philippine Congress (Sison 2013; Pasion 2017), among others.

Networks have been used to characterize political systems such as the United States Congress (Zhang et al. 2008) and the Philippine House of Representatives (Sison 2013; Pasion 2017). In such networks, nodes are the legislators themselves and links between them are either voting patterns or co-sponsorships of bills and resolutions (Sison 2013; Pasion 2017). These co-sponsorship networks can be used as proxies for effective political party affiliation of the legislators which can be derived from calculating partitions, also known as communities, which arise from their level of partisanship (Zhang et al. 2008).

On 14 Aug 2013, the Commission on Audit (COA) Special Audits Office Report No. 2012-03 (COA SAO 2013) on the priority development fund (PDAF) was released. The PDAF

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is a discretionary fund meant for legislators to fund smaller local infrastructure in their community projects outside of the national infrastructure plan (Nograles & Lagman 2012). Allegedly misused, it drew considerable attention as an exposé highlighted how these government funds were used, which led to reforms including the end of the PDAF itself. Even now, criminal cases are being pursued due to alleged misuse of the funds (Marcelo 2018). Despite these, we believe that it can still tell us more about the Philippine political system. We can say this based on studies of other countries’ political systems. One notable study was done by Zhang in 2008 on the US congress (Zhang et al. 2008). There, they were able to recover the political party structure by creating a network from the co-sponsored bills of US congressmen and senators.

What could a similar analysis reveal about the Philippine system? One topic of interest is the nature of Philippine political parties. There are two theories on how they emerged and why they are unable to take consistent political positions as noted by Montinola (1999). First is the classical view that the politicians are beholden to local factions and interests who expect favors in exchange for support (Lande 1968). Second is the structural view that this weakness is due to the very structure of the Philippine political system being unfriendly to the opposition party (Manacsa & Tan 2005). In line with the first theory, the PDAF being one of the primary ways a legislator can divert funds to a specific locality, one should expect that structures within the funding network to reflect political structure. From the second theory, one might expect the ruling party to dominate the system.

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 can be tackled through network visualization.

In general, these nodes or actors can be persons, groups, or organizations. In the current work, nodes were determined as the individual senators and congressmen, as well as the NGOs that received their PDAF allocations.

METHODOLOGYThe researchers took data from Annex A of the COA special report (COA SAO 2013) and converted this into a table that can be processed in an open source network analysis program, Gephi (Bastian et al. 2009).

We then made three visualizations of the data. The first is constructed by connecting a legislator to an NGO if

the legislator has funded that NGO. This is known as a bipartite graph, a graph wherein there are two classes of nodes that only connect to each other. We then have the two unipartite projections of this graph. First is the network built from legislators who have released funds to a common NGO and second is the network where NGOs who received funds from the same legislator is linked together. We use the graph layout algorithms in Gephi to present these networks.

The most common way to detect structure in a network is though community detection. A community in a network is a set of nodes more likely to connect with each than you would expect in a random graph. Long of interest to sociologists, the development of community detection algorithms was given prominence with the work of Girvan and Newman in 2004. Other work by physicists include Palla and co-authors (2007), who have found that different communities have different behavior based on their sizes; plus Leskovec and co-authors (2008), who have developed statistical techniques for analyzing the community structure of large social networks.

Community structure has been linked to political groupings in other political networks (Zhang et al. 2008). How well any given partition represents a community can be measured by the modularity Q (Newman 2006). An entire class of community detection algorithms works by attempting to find the partition that will maximize Q. It is given by:

12𝑚𝑄 � �𝑖𝑗 𝐴𝑖𝑗

𝑘𝑖𝑘𝑗 �𝑠𝑖𝑠𝑗 �� 2𝑚 (1)

where is 1 if nodes and are connected and zero;

otherwise, is the number of nodes connected to node

, also known as the degree of node ; is the total

number of edges in the network; and is one if node

and are part of the same partition and zero otherwise. Modularity has already been linked to real world meaning, corresponding to political partisanship in the United States Congress.

We maximize this value using community detection algorithms to determine if there were groups of nodes that tend to cluster together. The network’s nodes were then colored based on what “community” they fell into. We used the Louvain algorithm (Blondel et al. 2008) to detect communities in the PDAF network. One important shortcoming of the Louvain algorithm is that it is not deterministic. Because the Louvain algorithm is a modularity maximization technique, it shares the problem that there are an exponential number of distinct partitions whose modularities are close to the global

Philippine Journal of ScienceVol. 147 No. 3, September 2018

Sison et al.: NGO Co-funding Networks of PDAF Releases

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maximum (Fortunato 2010). Each run of the algorithm may result in one of these partitions. It is also not designed for cases wherein nodes can have multiple, overlapping memberships. We find this limitation acceptable as legislators are part of only one party. Nevertheless, we found that these partitions often differ by only the membership of small number of nodes.

The community structure is the most relevant metric to the theories of party formation in the Philippines. If party structure was dictated by similar causes, we would expect the parties to be reflected in the communities just as in the case of the US Congress’ bill co-sponsorship network. On the other hand, if the local interest hypothesis is true, we should find NGO’s that specialize around geographic areas to be of importance. If the ruling-party dominates is

true, we would expect the ruling party to be part of most communities in the network as each community would want the support of some of the ruling party for political capital and power.

We also used various network metrics to analyze the networks. We used betweenness centrality to determine influential nodes. The betweenness centrality of a node

given by

𝑔 (�) � �

� �𝑠��𝜎𝑠� (�)

𝜎𝑠� (2)

Betweenness centrality is the number of shortest paths between nodes in the network that go through a given node

Figure 1. Legislator-NGO network. Legislators and NGOs are represented by nodes. A legislator and NGO are connected together if the legislator funds an NGO. This is known as a bipartite network, where there are two classes of nodes the only connect to each other. Legislators are colored pink while NGOs are colored green. Inset: a labeled example of how nodes are linked together. The data for this can be found at https://sites.google.com/a/nip.upd.edu.ph/pdaf-data/home/data.



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, divided by the total number of shortest paths

in the network between those two nodes, .

Another method to determine influential nodes is eigenvector centrality, which works by taking the eigenvector of the adjacency matrix of the network. It is given by:

𝐀𝐱 � �𝐱 (3)

Here 𝐀 is the adjacency matrix of the network. � can be any eigenvalue, however the requirement that 𝐱 be strictly positive often forces � to be greatest eigenvalue. The idea behind eigenvalue centrality is that connections to more connected nodes provide more “value” than connections to less connected ones.

We also used the clustering coefficient, which is a measure of how likely nodes tend to cluster together in a network or how likely any given node in the network is connected to its neighbor’s neighbor. It is given by:

𝐶global � 3 x number of triangles

number of connected triplets of nodes(4)

However, the clustering coefficient was developed with networks with one kind of node in mind. Note that the clustering coefficient is defined by the number of “triangles” or sets three nodes fully connected to each other. As nodes of the same type cannot connect to each other, no triangles can form and thus, the clustering coefficient is zero in the bipartite graphs. We instead use, following Latapy and co-authors (2008), extension of clustering coefficient to bipartite graphs. First, they defined a clustering coefficient for two nodes to be:

(5)

Here the numerator is the number of common neighbors

of nodes and , while the denominator is the total

number of neighbors of nodes and . Both and must be members of the same class i.e., both of them must be legislators or both NGOs. They defined the clustering coefficient as the average of this measure computed for

paired with all other nodes in the same class that have

at least one common neighbor with .

(6)

Now, the measures themselves are simply not enough. We need a frame of reference to be able to determine whether these values are significant or not. In this regard, we turn to random graph models. By comparing the measures in our real network to the random network, we say whether the structure of the network is similar to randomness or due to underlying mechanisms. It is important that we do our analysis of clustering in the original bipartite network. Newman and co-authors (2001) has found that unimodal projections of bipartite graphs have much higher clustering coefficients such that random graph models are no longer applicable. However, we are still interested in these unimodal projections for their utility in examining legislator-legislator and NGO-NGO interactions.

In this work, we use two random graph models. First, the simplest random graph model is the Erdos-Renyi model,

which lays out connections in between nodes. We used

the bipartite extension with three model variables, nodes

of the first kind, nodes of the second, and randomly attached edges in between them. This model reflects a hypothetical where all legislators are identical and are equally generous in terms of NGO funding. We set these variables to the empirically observed values: 271 nodes, 189 legislators, 82 NGOs, with 383 edges between them.

The second model that we used is the bipartite extension of the configuration model (Saracco et al. 2015), which preserves the degree sequences of the two bipartite sets. This model preserves the reality that different legislators are more or less willing to use their PDAF. We took the existing degree sequences of the legislators and NGOs from the bipartite graph and used them as parameters for the model.

We then generated 30,000 random model samples with equivalent number of nodes and edges and compare the average measures to that of the real network.

DISCUSSIONFrom the bipartite network, we can see that the average legislator in the COA report funded 2.02 NGOs on average while the average NGO is funded by an average of 4.6 legislators. We also see 12 different disconnected NGO-legislator pairs, each only composed of one legislator and one NGO, separate from the rest of the network. The average bipartite clustering coefficient of the network is 0.306, more than two standard deviations from that of Erdos-Renyi bipartite graphs, 0.218 +/- 0.011. However, when we consider bipartite random graphs, we find that the clustering coefficient is well within two standard deviations (0.29 +/- 0.011). We also consider each set of



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nodes independently and find that the average clustering coefficient of legislator nodes is 0.379, while NGO’s have an average of 0.137. These values are more than two standard deviations from the one mode projections of Erdos-Renyi bipartite graphs’ clustering coefficient, 0.26 +/- 0.15 for the legislators and 0.12 +/- 0.0047 for the NGOs. However, we observe that these values fall within one standard deviation of the configuration model’s (0.363 +/- 0.128 and 0.128, +/- 0.020, respectively). We can conclude that the clustering coefficient can be explained by the difference in activity levels of the legislators.

We also observe that we can find a community partition with a modularity (0.69) that is significantly higher than a random graph from the configuration model’s (0.64 +/- 0.0085). However, groupings of legislators and NGOs are harder to understand so we check if we can see these groupings in the projected graphs.

First is the legislator-legislator network in Figure 2. We find that the network has an average degree of 20.9, which means that the average legislator has funded an NGO with 20.9 other legislators across all his funded projects.

Figure 2. Legislator network based on PDAF allocated to NGOs. Nodes are legislators, connected to each other if they have co-funded an NGO. Nodes are colored based on detected communities. The data for this can be found at https://sites.google.com/a/nip.upd.edu.ph/pdaf-data/home/data

We show in Figure 2 the legislator network and their detected communities. We found 6 to 7 (due to the uncertainty in modularity maximization) major communities with modularity of 0.478. The high value of modularity – compared to the configuration model network (0.42 +/- 0.024) – indicates a strong community structure, which we can also observe from visual inspection. There are a few legislators, such as Arrel Olano and Juan Ponce Enrile who are between communities. Indeed, re-running the algorithm will sometimes place these nodes into other communities. When we compare the community memberships to the party membership in Figure 3, we see that there is little correspondence between the divisions. To quantify this observation, we use the normalized mutual information (NMI) as proposed by Dannon and co-authors (2005). It is given by

(7)



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Where 𝑵 is the confusion matrix where rows correspond to communities belonging to partition 𝑨 and columns correspond communities belonging to partition 𝐵. Each matrix element 𝑁𝑖𝑗 corresponds to the number of nodes common between that row and column. The number of communities in partitions 𝐴 and 𝐵 are 𝐶𝐴

and 𝐶𝐵, respectively. 𝑁𝑖⋅ and 𝑁⋅𝑗 are number of nodes in communities 𝑖 and 𝑗 of partition sets 𝐴 and 𝐵, respectively.

The NMI reflects the common information between the two partitions. If they are identical, it is 1 and it is 0 if they are completely different. We then compared the party partition with the detected community partition. When considering the independents as one party (reflecting a coalition), we find that the parties have an NMI of 0.14 with the detected communities. If we consider the independents as each a member of a one-person party, this gives an NMI of 0.26 with the detected partition. However, when we consider the all the legislators as a

member of their own one-person party, this partition has an NMI with the detected community partition of 0.54. This implies that the communities in the network have more in common with a partition that fully separates the legislators than one that groups them into parties. We also tried first removing the disconnected nodes before running community detection. This results in an NMI of 0.088 between the detected communities and the party partition with combined independents, an NMI of 0.16 between the detected communities and the party partition with separate independents, and an NMI of 0.48 between the detected communities and the separated legislator case.

Of note, we also observe that there are members of the ruling party (LAKAS, colored red) in most of the communities, supporting the structural view in that the opposition is forced to cooperate with the ruling party.

We also rank the legislators based on their degree, of which Adam Relson Jala tops the list. These most

Figure 3. Legislator network based on PDAF allocated to NGOs. Nodes are colored based on party association during the 14th Congress. The data for this can be found at https://sites.google.com/a/nip.upd.edu.ph/pdaf-data/home/data.



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Figure 4. Network structure of NGOs. NGOs are linked if a legislator allocates their PDAF to both of them. Nodes are colored based on communities detected. The data for this can be found at https://sites.google.com/a/nip.upd.edu.ph/pdaf-data/home/data.

connected legislators are the one with the most funding partners, either due to funding lots of small projects with other legislators or participating in multiple big projects funded by multiple legislators.

In order to determine the most important legislators, we turn to centralities. Arrel R. Olano is closely followed by Sen. Juan Ponce Enrile in having the highest betweenness centrality, while Adam Relson Jala has the highest

eigenvector centrality. See Table 1 for more details. High betweenness centrality indicates a tendency to bridge disparate groups of legislators and could indicate legislators who have supported a diverse array of NGOs. High eigenvector centrality, on the other hand, indicates connections to well-connected legislators. The fact that Jala, who has the most connections, has the highest eigenvector centrality reflects this.



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Table 1. Various network parameters in the legislator network for the top 10 legislators based on their betweenness centrality. Betweenness centrality is defined as the number of shortest paths from all vertices passing through a node. The eigenvector 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 centrality measures are normalized in this table. In the final column, we also show how central these nodes are in the 14th Congress’ co-authorship network.

Name Betweenness Centrality Eigenvector Centrality Percentile Rank in Co-authorship network

Arrel R. Olano 0.09 0.73 40.1

Juan Ponce Enrile 0.09 0.27 N/Aa

Adam Relson L. Jala 0.05 1.00 73.8

Francisco T. Matugas 0.05 0.76 84.6

Ignacio T. Arroyo. Jr. 0.05 0.73 43.8

Emmanuel Joel J. Villanueva 0.03 0.45 14.6

Samuel M. Dangwa 0.03 0.28 61.8

Marc Douglas C. Cagas IV 0.03 0.49 38.6

Magtanggol T. Gunigundo I 0.03 0.08 1.50

Rozzano Rufino B. Biazon 0.03 0.06 47.9aJuan Ponce Enrile was a senator and hence not part of the house’s co-authorship network.

An interesting question arises: do these patterns and structures among legislators correspond to how legislators legislate? In our previous work (Sison 2013; Pasion 2017), we studied the bill co-authorship network of the House of Representatives of the 8th to 14th Philippine congresses. In that work, nodes correspond to legislators and these legislators are connected to each other if they co-authored a bill. One way to answer the previous question is to see if the list of most central legislators in the PDAF network corresponds to the most central legislators in the bill co-authorship network of the 14th Philippine Congress. The results can be seen in Table 1. As we can see, there is very little correspondence in the most important legislators in the PDAF network and the bill co-authorship network.

To further these comparisons, we plotted the percentile ranking of all present legislators’ eigenvector centrality in 14th Congress’ co-authorship network against their eigenvector centrality, degree, and betweeness centrality in Figure 5. It can clearly be seen that there is no correlation. Perhaps this is because only a fraction of the legislators participated in the PDAF. Another possible reason is that how the fund and how they legislated is driven by different mechanisms. Finally, the fact that the senators, like Juan Ponce Enrile, cannot participate in the bill co-authorship network may also change the dynamic.

On the other hand, the NGO network shows an average degree of 6.53, which implies that NGOs share sponsors with 6.53 other NGOs. The NGO network has 82 nodes with 268 edges. The first notable feature is the existence

Figure 5. Scatter plot of various network measures versus the eigenvector centrality ranking in the legislative co-authorship network. Only congressmen from the 14th Congress are included. Forty-eight (48) legislator nodes, from the 13th Congress and senators were excluded from this plot. No correlation is observed.



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Table 2. Various network parameters in the NGO network for the top 10 NGOs based on their betweenness centrality. The centrality measures are normalized in this table.

NGO Betweenness Centrality

Eigenvector Centrality

Kabuhayan at Kalusugan Alay sa Masa Foundation. Inc. (KKAMFI)

0.15 0.68

Kagandahanng Kapaligiran Foundation. Inc. (KKFI) 0.12 0.82

Dr. Rodolfo A. Ignacio. Sr. Foundation. Inc. (DRAISFI) 0.11 1

Farmerbusiness Development Corp (FDC) 0.07 0.71

Masaganang Ani Para sa Magsasaka Foundation. Inc. (MAMFI) 0.06 0.97

Aaron Foundation Philippines, Inc. (AFPI) 0.06 0.50

Pangkabuhayan Foundation. Inc. (Pang-FI) 0.06 0.84

Table 3. List of network parameters.

Network

Nodes EdgesAverage Degree

Average Clustering Coefficient

Maximum Modularity

Number of Detected Communitiesa

Bipartite Network 271 383 2.82 0.306 0.7 13

Legislators 189 2.02 0.379 N/A N/A

NGO 82 4.6 0.137 N/A N/A

Legislator-Legislator Network

189 1924 20.36 0.817 0.5 6-7

NGO-NGO Network 82 268 6.54 0.661 0.4 4aOnly considering nodes part of the largest connected component

of several disconnected nodes. These are NGO’s that have been funded by a single legislator therefore they cannot share sponsors with another NGO so they become disconnected. Of the connected NGO’s, we can observe four (4) communities. The communities are all eclectic mixes of various causes with no apparent common cause behind them. This weakens the local interest hypothesis. Full analysis of this is difficult, as many of these NGOs have very little known about them. The NGO network has a significantly higher modularity (0.433), which is more than five standard deviations greater than the null configuration model’s (0.31 +/- 0.023). This indicates that these groupings are of some significance.

We could also apply some other network techniques such as looking at which node are important nodes in the NGO network. This is measured by the eigenvector centrality (Bastian et al. 2009), which measures their importance. The NGO which has the highest eigenvector centrality is the Dr. Rodolfo A. Ignacio. Sr. Foundation Inc. (DRAISFI), an NGO. This is followed by the Masaganang Ani Para sa Magsasaka Foundation Inc. (MAMFI), Countrywide Agri and Rural Economic Development (CARED) Foundation Inc, and Social Development Program for Farmers Foundations Inc.

(SDPFFI), respectively – all of which have measures of eigenvector centrality greater than 0.90 (see Table 2 for more numbers).

CONCLUSIONWe can see that the network representation is able to reveal non-obvious patterns in the behavior of legislators. From the bipartite clustering coefficient, we can say that legislators who funded an NGO together are more likely to be collaborators in future projects and NGO patrons tend to come in certain groups. Further, we see that the parties of the legislators do not determine who they will work with and that they try to include members of the ruling party in their groups. NGOs with similar patrons also do not have any obvious connections in their causes. The appearance of the ruling parties in all communities supports the ruling party dominates hypothesis, while the NGO network’s structure tentatively weakens the local-interest hypothesis. Ultimately, these results allow us to see the structure and properties of legislators’ working relationships. While there are hints as to what structures represent and what they mean for the public, verification of these hints is the topic for a different work.



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ACKNOWLEDGMENTSWe would like to acknowledge the Instrumentation Physics Laboratory where the data gathering and part of the analysis was done. We would also like to thank Gilbert Chua for proofreading.

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