Identifying the Collaboration Relationships in COINs
from Photos
Qi Wen, Tsinghua University and MIT Center for Collective Intellgience
Wenq10@mit.edu

Introduction
With the rapid development in electronic devices, the days when we took only a limited number of photos
and treasure them in beautiful frames have already gone. Nowadays, we become used to take more and morepictures with electronic devices (cameras, smartphones, tablets, etc.) to record our activities, but are spending lessand less time seriously reflecting on the pictures and the underlying activities. In this sense, we are more capableof recording our activities but less capable of deriving insights from them, and this issue is especially relevant inCOINs, where there are intensive collaborations among the members. This research develops an automatic
approach to identify the collaboration relationships in COINs from photos and gain insights from the collaboration
network.

Methods to derive the meeting network
Through a collaborative research program with South-North Water Transfer Project Company, I collected thefull records (totally 157, from 2004 to 2016) of the meetings held during the construction phase of the project.
Five sequential steps were performed to process the data and derive the meeting-based collaboration network.

I. Face extraction
The faces of the meeting participants need to be extracted before they can be recognized by the facerecognition model. Using the haarcascades package in OpenCV [1], I developed a Python program to extract thefaces appearing in each photo taken during the meetings.

II. Model training
A deep learning based face recognition model (Convolutional Neural Network, CNN) was developed basedon the Keras [2] framework in Python (the model is represented as the robot in Figure 1). Since the meeting
participants are renowned fellows of Chinese Academy of Sciences, I was able to easily retrieve their photos fromthe Internet. These photos acted as the training and testing datasets of the face recognition model (each expert
with 15 photos as training data and 5 photos as testing data). Thereafter, I fed the training dataset into the facerecognition model and run the model training program (which is just like telling the computer “who is this in thisphoto”). The model has relatively strong capacity and achieved a satisfactory accurate rate of 90.83% on thetesting dataset (i.e. 90.83% of the photos in the testing dataset were correctly recognized). A human typicallyachieves an accurate rate of 92% in similar face recognition tasks. But considering the relatively large number ofmeetings and participants, it is a rather tough, if not impossible, task for human to remember the faces of 66
experts in ECSWT and then identify them from the 326 photos of 157 meetings. Thus, I adopted the trained facerecognition model in subsequent analysis.

12Source of the package: http://opencv-python-tutroals.readthedocs.io/en/latest/
More information on Keras neural network framework: https://keras.io/

III. Face recognition
Now that the model was trained, I utilized it to recognize the faces appearing in the meeting records. For
each meeting, the faces extracted from the photos in the meeting record were input to the face recognition model.
Then the model would give a prediction on who is in the photo (just like show the photos to the computer and thenask the computer to tell us “who is this in the photo that attended the meeting”). In this way, the participants ofeach meeting can be recognized, and a meeting-participant matrix (matrix 2) can be constructed.

Figure 1 The analysis procedure to derive the meeting network

IV. Validation by text information and interviews
In order to validate matrix 2, I also utilized the texts of the meeting records as another source of information.
With the help from the secretary of ECSWT, I obtained the name list of all the committee members. Using theexperts’ names as the keywords, I searched the meeting record texts and find the names appearing in the text
record of each meeting. Based on the information from the texts, another meeting-participant matrix (matrix 1)
was derived.

V. Combination
Comparing matrix 1 with matrix 2, I found that their overlaps mainly lie in the most renowned members.
That is to say, the most renowned members are reported in both the texts and the photos of the meeting records.
However, less renowned members are much less reported in the texts (this phenomenon is rather common inChina), and the photo data supplemented important information on their participation in the meeting, which may
be lost otherwise. In this sense, the photo and text data compensate each other, and hence, I combined matrix 1
and matrix 2 into the full matrix (matrix 3), based on which the meeting-based collaboration relationship can bemapped and further analyzed.

Network data visualization
Based on the derived meeting network, I used UCINet to map the meeting-based collaboration relationships
among ECSWT members. Figure 2 presents the 2-mode network of meetings (denoted as red circles) andparticipants (blue squares). If we define attending the same meeting as the network tie between two organization
members, we can derive the meeting-based collaboration network after each meeting (m) as shown in Figure 3.

Figure 2 The 2-mode network of meetings (red circles) and participants (blue squares)

Figure 3 The growth of expert collaboration network
Note: line width indicates tie strength, and node size is proportional to betweenness centrality value.

The implications of the network data
Using the time taken for each meeting to reach a conclusion as the dependent variable, I performed cox
regression on the network attributes of the participants of each meeting. The three subgraphs in Figure 4 show that,
more familiarity (previous collaboration experience) among participants is associated with more rapid decision;
more concentrated social capital (betweenness centrality measure) distribution is associated with more rapid
decision.

Figure 4 Survival functions at different levels of independent variables
Note: the analyzed variable equals 0 at the baseline level.
These results suggest that recruiting the members with desired network attributes (i.e. familiar with eachother and relational leadership) can contribute to the improvement in the timeliness of group decision making.