Given a directed social graph, have to predict missing links to recommend users (Link Prediction in graph)
Taken data from facebook's recruting challenge on kaggle https://www.kaggle.com/c/FacebookRecruiting
data contains two columns source and destination eac edge in graph.
- Data columns (total 2 columns):
- source_node int64
- destination_node int64
- Generated training samples of good and bad links from given directed graph and for each link got some features like no of followers, is he followed back, page rank, katz score, adar index, some svd fetures of adj matrix, some weight features etc. and trained ml model based on these features to predict link.
- Some reference papers and videos :
- No low-latency requirement.
- Probability of prediction is useful to recommend highest probability links
- Both precision and recall is important so F1 score is good choice
- Confusion matrix
Start by downloading the project and run "Facebook_Link_Prediction_Models.ipynb" file in ipython-notebook.
You need to have installed following softwares and libraries before running this project.
- Python 3: https://www.python.org/downloads/
- Anaconda: It will install ipython notebook and most of the libraries which are needed like sklearn, pandas, seaborn, matplotlib, numpy and scipy: https://www.anaconda.com/download/
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scikit-learn: scikit-learn is a Python module for machine learning built on top of SciPy.
- pip install scikit-learn
- conda install -c anaconda scikit-learn
-
networkx: NetworkX is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks.
- pip install networkx
- conda install -c anaconda networkx
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nltk: The Natural Language Toolkit (NLTK) is a Python package for natural language processing.
- pip install nltk
- conda install -c anaconda nltk
โข Manish Vishwakarma - Complete work