Protein-protein interaction networks are known. It is an undirected graph. Each row of the networks is like this (Protein 2 - Protein 6), and It represents the interaction between Protein 2 and Protein 6.
networks: Protein 2 - Protein 6 Protein 4 - Protein 5 Protein 6 - Protein 5 Protein 5 - Protein 7 ...
In this network, the function of some proteins are known, and proteins with similar function tend to be relevant.
The function of some proteins: Protein 2,Func_002 Protein 2,Func_007 Protein 2,Func_008 Protein 3,Func_007 Protein 3,Func_008 Protein 3,Func_009 Protein 4,Func_011 Protein 5,Func_015 ...
And It is known that a part of proteins are cancer-related proteins,
The known proteins: Protein 4,Cancer Protein 6, Cancer Protein 7, Cancer Protein 10, Cancer ...
But the vast majority of proteins is unknown whether is cancer-related protein or noncancer-related protein. How can you use the known cancer-related proteins and nonCancer-related proteins to predict the protein whether is or not a cancer-related protein?
I do not know how to solve this problem.
Dear Jean-Karim Heriche, how to understand the result of label propagation algorithm? Thank you. This is my question. http://stackoverflow.com/questions/34701650/how-to-understand-the-result-of-label-propagation-algorithm
This is related to diffusion. Imagine that your labelled nodes are labelled with some blue color and you let this color diffuse through the edges and the higher the edge weight the more color goes through it. What you compute can be thought of as being the amount of blue that reaches the unlabelled nodes at equilibrium. For more than one class, imagine that you have red and blue nodes and that both colors diffuse through the edges in proportion of the edge weight then the resulting matrix can be viewed as giving the amount of red and blue at each unlabelled node.