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๐ŸŽƒ About Me

I am a Data Science PhD student who lives in sunny Edinburgh ๐Ÿฐโ˜” and creates machine learning tools which solve non trivial problems related to graph structured data . Currently I am mainly working on Karate Club, a library built on NetworkX which allows you to perform the most standard unsupervised learning tasks on graph structured data. Karate club supports community detection, network embedding and graph summarization procedures. I also maintain repositories which curate content about machine learning research such as decision trees, gradient boosting, fraud detection, and graph classification. When you decide to be my Github Sponsor, you support the development of these data mining tools and the maintenance of these open source materials.

๐Ÿš€ Mission

My mission is to create a scikit-learn like library for unsupervised learning from graph structured data with an API driven design. There exists no out-of-the-box tools for unsupervised machine learning from graph structured data. By using an easy to use tool people could extract value from linked webpages, molecular graphs, financial transaction series, and social networks with ease.

๐Ÿ’ฐ Funding

Maintaining the flagship graph mining package, smaller graph neural network projects, and the curated machine learning repositories is a time consuming process. These projects are only tangentially related to my research. Currently I am doing all of these these without any external financial help. Getting funding would be truly beneficial for me and the machine learning community.

๐Ÿ“‘ Goals

First, I would like to release the first stable and tested version of Karate Club which covers a wide variety of community detection, node embedding and network description algorithms. As part of this I would benchmark the performance of these algorithms on new larger datasets. Specifically I would like to include scalable and accurate algorithms which can perform:

  • Neighbourhood-based node embedding
  • Attributed node embedding
  • Structural node embedding
  • Whole graph embedding (network summarization)
  • Overlapping community detection
  • Non-overlapping community detection

Second, I would like to develop a scikit-learn like sparsity-aware matrix factorization package which can be used in recommender systems and other sparse data scenarios. This would be beneficial both for researchers and practitioners.

Finally, the research paper aggregator repositories require frequent updates. With additional resources I could allocate more time to do more frequent updates.

๐ŸŒŒ To Infinity and Beyond

My long term plan and dream is to create a company that would offer graph mining, geometric deep learning and network science consulting and services. I am not there yet, but tools like Karate Club, Graph2Vec and Walklets are small but important steps in that direction.

๐Ÿ’– Thank you!

Thank you for your consideration!

5 sponsors have funded benedekrozemberczkiโ€™s work.

Private Sponsor
@MridulS
@terragord7
@MarcoLomele
@taskswithcode

Featured work

  1. benedekrozemberczki/GEMSEC

    The TensorFlow reference implementation of 'GEMSEC: Graph Embedding with Self Clustering' (ASONAM 2019).

    Python 254
  2. benedekrozemberczki/karateclub

    Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)

    Python 2,182
  3. benedekrozemberczki/littleballoffur

    Little Ball of Fur - A graph sampling extension library for NetworKit and NetworkX (CIKM 2020)

    Python 706
  4. benedekrozemberczki/shapley

    The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021).

    Python 219
  5. benedekrozemberczki/awesome-graph-classification

    A collection of important graph embedding, classification and representation learning papers with implementations.

    Python 4,770
  6. benedekrozemberczki/pytorch_geometric_temporal

    PyTorch Geometric Temporal: Spatiotemporal Signal Processing with Neural Machine Learning Models (CIKM 2021)

    Python 2,705

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$5 a month

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Pomelo tier ๐ŸŠ | This gets me a pomelo (a large citruis fruit) from the nearby grocery store. It also gets you:

  • A sponsor badge ๐Ÿฅ‡ on your profile.
  • Access to my monthly newsletter with recent developments.
  • My gratitude. Thank You!

$10 a month

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Avocado wrap tier ๐Ÿฅ‘ | This actually gets me a typical grad student lunch at my favorite Sudanese wrap place. It also gets you:

  • All the previous tier rewards.
  • Your name under sponsors in the README.md of a chosen repository.

$25 a month

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Bag of coffee tier โ˜• | This is a bag of freshly roasted fair trade coffee that I can share with others. It also gets you:

  • All the previous tier rewards.
  • Priority response to issues related to my repositories.

$50 a month

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Gym membership tier ๐Ÿšฃโ€โ™‚๏ธ | This actually gets me a monthly pass to the Edinburgh Leisure so I can overuse the rowing machine and have back pain. It also gets you:

  • All the previous tier rewards.
  • Your name under sponsors in the README.md of a chosen repository.
  • Priority response to issues.

$100 a month

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Buddy tier ๐Ÿ‘ | This involves actual work with you on your project (non corporate)! You get:

  • All the previous tier rewards.
  • Discussing graph mining problems and graph neural network methods related to your own open source project.
  • Taking the above into account, this is about 1hr a month of discussion.

$200 a month

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Corporate tier ๐Ÿ•ด๏ธ| This involves actual work for companies! You get:

  • All of the previous tier rewards as a corporate client.
  • Discussing graph mining problems and graph neural network methods.
  • Taking the above into account, this is about 1hr a month of general consultation/support.