volunteer_data:meetings

Recommendation and social network analysis

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Meeting Notes

Meeting: Jan 17, 2022

Presented results of experiments on Volunteer data and Movielens
Discussed the metrics for measuring performance on the two datasets
Showed graphs of two methods and their performance on the two datasets

Key: the BOA method still outperforms LDA in both datasets.

Comments and Future work
To compute metrics as a whole and not aggregate them separately for each user.
To apply BOA method on a recommendation paper and compare the results
To compile a representative code and send to the professor
To read literature on latest work in RS.

Meeting: Jan 24, 2022

Discussed the possible alternatives of embedding the data, social net embedding
Refined the life document to include user history pertaining to time, location and participation history
Discussed a few papers that have used GNN for prediction

Comments and Future work
To read the papers related to Social Recommendation eg, Song et al (2019)
Understand the nature of embeddings they used for the input data
Summarise findings of papers (using GNN) whose data is similar to ours

Meeting: Feb 18, 2022

Presented a Survey on GNN Social recommendation
Presented Item-to-Item KNN method used by Amazon
Discussed two papers on Social Recommendation and presented their findings
Paper 1: GNN for Social Recommendation - Fan, Ma, Li, He,Zhao,Tang, and Yin. 2019. (ACM, WWW)
Paper 2: Session-based Social Recommendation via Dynamic Graph Attention Networks, Song, Wang and Xiao (2019) – (ACM, WSDM)

Comments and Future work
To begin working on experiments related to dynamic session-based recommendation
To make a list of the features and embeddings that can be used from our data,
Determine session and neighbours based on our data, eg location based or participation based.

Meeting: March 1, 2022

Attendees:
Professor Yang Li, Shutong and Tim (19:00-20:00)
Discussion
Tim presented a proposal on GNN

Comments and Future work
To clarify the research problem by formulating the underlying mathematical nitty gritties
To consider geographic location and tasks types as input features and determine how to incorporate them

Meeting: March 23, 2022

Attendees:
Professor Yang Li, Shutong and Tim (11:00-12:00)
Discussion
Shutong presented and discussed the plan on how the experiments will be conducted.
Tim presented the findings on the first experiment for Session based GNN

Comments and Future work
Tim to write a brief methodology discussing his implementation of the algorithm
Need for providing details for experimental plan and hyper-parameter tuning

Meeting: March 31, 2022

Attendees:
Professor Yang Li, Shutong and Tim (11:00-12:00)
Discussion
Tim presented a write up of methodology section that also included experiment plan
Comments and Future work
Tim to give more clarity on the network architecture diagram
To consider node embeddings as one hot key vectors
To also include task type information on the volunteer preference
Tim to remind Shutong and the professor every week before each meeting.
Shutong suggested Tim to use PPT to draw flowchart and diagrams
Shutong suggested to provide reference code for matching task types

Meeting: April 7, 2022

Attendees:
Professor Yang Li, Shutong and Tim (11:00-12:00)
Discussion
Shutong presented her preliminary experimental results
Tim discussed the DGRec framework.
Comments and Future work
Shutong to continue with her experimental plan
Shutong to consider narrowing the research towards a more specific problem
Tim to conduct experiment and reproduce the results in the paper
Attachments:
shutong_experiments.pptx
graph_attention_network.docx

Meeting: April 14, 2022

Attendees:
Professor Yang Li, Shutong and Tim (11:00-12:00)
Discussion
Shutong presented statistical data for Pioneers to be used in an article
Tim presented the reproduced results from the paper
Comments and Future work
Shutong to write a summary of her latest findings
Tim to consider a naive baseline approach that will be compared against the current method
Attachments:
tim_progress_4.14.pptx

Meeting: April 21, 2022

Attendees:
Professor Yang Li, Shutong and Tim (11:00-12:00)
Discussion
Shutong presented a summary of her proposal and latest findings
Tim compared the performance of voting process/UserKNN against the model of interest
Comments and Future work
Tim to explore at least one or two graph models for further comparison
Shutong to make recommended adjustments to the presentation
Attachments:
tim_progress_4.21pptx.pptx

Meeting: May 16, 2022

Attendees:
Professor Yang Li, Shutong and Tim (14:00-15:00)
Discussion
Shutong presented an updated version of her research findings, challenges and proposal. Greedy algorithm solves some of the common challenges in community detection.
Shutong discussed latest findings on NCE and community detection for Futian and Nanshan districts
Tim showed comparative results for DGRec and GR4URec. DGRec still outperforms baselines such as UserKNN
Comments and Future work
Shutong to determine the relationship between SHS and NCE results
Shutong to summarize charts and send them to Pioneers in order to get feedback on changes that correspond to the detected time slots
Shutong to make minor recommended adjustments to the presentation such as illustrating the important next steps as a pipeline instead of explaining them in text.
Tim to analyze data to determine the optimal time window that gives dynamic sequences
Tim to conduct experiment at district level, for instance using Futian and/or Nanshan data

Meeting: May 23, 2022

Attendees:
Professor Yang Li, Shutong and Tim (14:00-15:00)
Discussion
Tim presented statistical information to determine appropriate time window
Based on standard deviation and entropy, Futian and Nanshan had highest metrics at k=7 and k= 28, respectively.
Shutong discussed the relationship between NCE, SHS and S-NCE
Shutong reported that C-NCE has positive correlation with the number of tasks and volunteers, meaning that when the number of tasks/volunteers increase, the system becomes unstable. C-NCE also is positively correlated to the number of SHS [for more details, see attachment]

Comments and Future work
Shutong encountered an efficiency problem where its taking longer to compute results for Futian
Shutong to show her computational method to pave way for alternative solutions
Tim to consider double checking the data since the number of unique organisers keeps decreasing/constant as time window increases.
Tim to conduct experiment at district level with induced structural breaks in the data

Attachments
Shutong: shutong_cnce.pptx

Meeting: May 30, 2022

Attendees:
Professor Yang Li, Shutong and Tim (14:00-15:00)
Discussion
Tim presented updated statistical information to determine appropriate time window. The average of Std and Entropy yielded a prediction window of 14 days. Structural breaks were used to determine breakpoints. DGRec with 14 day window was then trained on the breakpoints and results were presented for Futian district.
Model performance during low volunteer turnout period is low, implying that when participation is low, volunteers behavior is not deterministic.
Shutong discussed the meeting minutes from her previous presentation with another collaborating professor.

Comments and Future work
Tim to come up with hypothesis on factors that influence the participation of volunteers during low turnout period, ie, breakpoint 4.In the long run, to think about volunteer retention.
Tim to extract weights from the model and analyze them
Tim to keep doing experiments and compare the performance of DGRec with GR4URec
Also report statistics of the total number of participants per each breakpoint.
Instead of improving the model, think of how we can use it to analyze volunteer behaviour

Meeting: June 13, 2022

Attendees:
Professor Yang Li, Shutong and Tim (14:00-15:00)
Discussion
Tim presented an analysis of hypotheses on volunteer participation. H1: The distribution of task types impacts daily volunteer participation, H2: Distribution of task types impacts model prediction.
It was also noted that task types that appeared as 'noise' were not significant in OLS BP4 regression
Shutong shared an outline of the experiments that she intends to do in the coming days/weeks.

Comments and Future work
Tim to construct hypotheses around improving model prediction accuracy and not sway far away from the main problem.
Tim to consider directly testing if and how task types or volunteer participation impacts model prediction instead of applying indirect convolutions
Tim to write a draft paper
Shutong to follow up and execute the next tasks according to the experiment plan
Shutong and Tim to work on PPTs for weekly group meeting scheduled for June 23rd

Meeting: June 27, 2022

Attendees:
Professor Yang Li, Shutong and Tim (14:00-15:00)
Discussion
Tim shared his plan for the next steps. It includes Paper writing and Ablation study.
Ablation study will focus on determining the impact of long v short term interests, impact of increasing neighbours and difference between individual and neighbours' interests
Shutong shared her plan for the next experiments. Some of the outstanding work include in-depth analysis of SHS, stability and finding benchmarks

Comments and Future work
Tim to separate ablation study from parameter based experiments such as the impact of increasing neighbourhood.
Tim to prioritise experiments and research conferences for submission.

Meeting: July 11, 2022

Attendees:
Professor Yang Li, Shutong and Tim (14:00-15:00)
Discussion
Shutong shared latest experiments including an overview of updated dataset (2022 data) with labels.
Shutong showed an extensive study on the relationship between SHS and evolution of communities over time. Using interesting visuals, she also discussed a case study of Nanshan district. Unfortunately Nanshan district seem to not have volunteer data for a prolonged period of time, potentially affecting the results.
Tim briefly showed that the impact of increasing neighbors on recommendation accuracy is not very high. Tim also pointed a challenge regarding determining the impact of tasks types on recommendation.

Comments and Future work
Shutong
Shutong to inquire more about the missing participation data for Nanshan district (2020.10.1-10.20)
Shutong to determine how does community size affect the relationship between SHS number and evolution type
Shutong to fine-tune FaceNet community detection algorithm parameter, for instance, by increasing alpha
On the Hypothesis: SHS increases merge probability:
Shutong to conduct a case study on two merging communities in order to:
Determine whether task type or other behaviors are becoming similar after the merging event
Analyze the position of SHS, for example, does it connect to higher degree nodes than other users?
Finally, Shutong to review 2-3 papers on recent SHS and dynamic community detection
Tim
Tim to check the effect of task type on the recommendation result
Tim to continue the ablation study experiments to determine:
The effect of neighbors in GNN
The effect of long term interest and short term interest of neighbours
The effect of individual interests without neighbourhood information

Thanks to Professor Li for helping jotting down the notes

Meeting: July 18, 2022

Attendees:
Professor Yang Li, Shutong, Zhang Anping, Kong Dexu and Tim (14:40-16:00)
Discussion
Shutong presented two papers on community detection.
1. Detecting the evolving community structure in dynamic social networksExternal Link
2. Structural holes and managerial performance: Identifying the underlying mechanisms External Link
Tim presented results from the paper that were produced by a different method: RNN Session
Using the same approach, Tim showed the effect of teasing apart individual interests from neighbours interests.
Adding neighbours improves the accuracy. However, continuously adding neighbours after 1 does not improve the results significantly.

Comments and Future work
Tim to do experiments and test the model at district level, particularly where the performance is not high. For example, Breakpoint Analysis showed that the model does not perform well under certain circumstances.
Thus, Tim should conduct similar experiments on district level to determine the effect of task types on model accuracy.
Tim to furnish the WOLT poster and send it to professor at least 2 days before the deadline (07.25)

Meeting: Aug 1, 2022

Attendees:
Professor Yang Li, Shutong and Tim (14:00-16:00)
Discussion
Tim presented results from break point analysis for Futian district. Results show that break point 4 had the worst performance, a recall around 0.64%. Within break point 4, task 3 (environmental topics) had the worst performance in the test data, recall around 0.40%
Shutong presented her latest findings after adjusting training parameters with FaceNet. She also showed the performance of different approaches and their impact on probability of communities growing, merging, disappearing etc.
Comments and Future work
Of concern was the breakpoint algorithm used. It seems to separate data into breakpoints that are inconsistent with natural observation from the graph. Tim to do the experiment again using a more objective approach to determine breakpoints.
Tim to also look for upcoming conferences and their deadlines.
Shutong to consider possible journals to publish the work

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