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latest code at github:
https://github.com/kwd2/graphnet/wiki
latest working draft:
https://docs.google.com/document/d/1nJzJwgtsViThUcien_4tXDAD-sU_6bfY37Iny8WQc0k/edit?usp=sharing
working draft:
https://drive.google.com/open?id=1xw6fSTbktPrf3XoaLd4ufSaQqOMqw-EDNiTAWNFmgJw
/ draft/
The graphnet project contains code and results of a data analysis technique that uses a recursive graph structure to analyze large volumes of data. The technique was initially developed to analyze large volumes of packet data generated by smartphones in the 3G/4G telecom networks. The method is generally applicable to data sets that consist of sequences of symbols, such as the words in a sentence or the letters in a word.
The paper and code below are to a graph analysis of the WordNet database:
course draft paper:
https://docs.google.com/document/d/1xw6fSTbktPrf3XoaLd4ufSaQqOMqw-EDNiTAWNFmgJw/edit?usp=sharing
code:
http://sourceforge.net/projects/sequencetree/files/notes.txt/download
http://sourceforge.net/projects/sequencetree/files/bica6.cpp/download
/ draft/
The current direction of the project is to build graph structures from large volumes of symbol sequences. Sequences can be of the general form [a,b,c,d], or [man, eat, meat]. Nodes of the graph correspond to each symbol, and connections
This is analogous to a baby that is exposed to spoken language and Human interaction and gradually learns to understand its input.
Iterative interactions with Humans can be also used as input. For example, the popular Internet forum Reddit contains a wealth of information written by millions of Humans. A basic direction of the current work is aimed at being able to read and understand such information. As part of this process, questions can be generated, which can be posted to Reddit and answered by Humans.
This is a recent (2013) paper that uses the graph technique on a Telcom related analysis. This describes the code and original motivation of the technique.
http://onlinelibrary.wiley.com/doi/10.1002/bltj.21651/abstract
The following links are to a very rough draft of a cognitive architecture paper based on this work:
http://sourceforge.net/projects/sequencetree/files/BICA1.docx/download
This paper makes use of a sparse representation of concepts as a cluster of nodes (inspired by the Jeff Hawkins/SDR model). This allows various cognitive functions to be modeled with a single underlying class.
The Wiki, Blog, and .ppt below contain early (non sparse) descriptions of the work.
http://sourceforge.net/projects/sequencetree/files/total_recall.pptx/download
Ken
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