function [ affinity ] = CalcPHsAffinityByAA( data, actual_graph_size, num_missing_nodes, connectPlaceholdersToNeighbors) % sigal 29.10.13 % based on CalculateAffinityByAdamicAdar_Sparse % adamic/adar(i,j) = sum( 1 / log(num_neighbors(k) | k is a % neighbor of both i and j fprintf('CalcPHsAffinityByAA: NNZ(data) = %d\n', nnz(data)); firstPH = actual_graph_size-num_missing_nodes+1; numPHs = size(data,1) - firstPH +1; %n = size(data,1); %diag_indices = 1:n+1:n*n; %data(diag_indices) = 1; %we consider each node as a neighbor of itself to obtain higher connectivity in the affinity matrix - i.e. each node will have a positive affinity to all its neighbors if connectPlaceholdersToNeighbors > 0 data = ConnectMissingNodesToNeighborsNeighbors( data, actual_graph_size, num_missing_nodes); end affinity = zeros(numPHs,numPHs); s_cols = sum(data(:,firstPH:end),2); % give the #PH neighbors per node (nx1)? fprintf('CalcPHsAffinityByAA: NNZ(s_cols) = %d\n', nnz(s_cols)); for k = 1 : size(data,1) if s_cols(k) > 0 %find all the neighbors of k neighbors_k_vec = data(k,:); neighbors_k_vec(k) = 1;%we consider each node as a neighbor of itself to obtain higher connectivity in the affinity matrix - i.e. each node will have a positive affinity to all its neighbors neighbors_k = find(neighbors_k_vec); num_neighbors_k = size(neighbors_k, 2); %sigal - only need to calcluate for PHs (thus indexes are updated accordingly) phs_neighbors_k_idc = neighbors_k>=firstPH; phs_neighbors_k = neighbors_k(1,phs_neighbors_k_idc)-firstPH+1; if num_neighbors_k > 1 && size(phs_neighbors_k,2) > 0 w = 1; inv_log_num_neighbors_k = w / log(num_neighbors_k); for i = phs_neighbors_k for j = phs_neighbors_k affinity(i,j) = affinity(i,j) + inv_log_num_neighbors_k; affinity(j,i) = affinity(i,j); end end end end end %sigal 29.10.13 - normolize by diagonal min v = diag(affinity); max_val = min(v); %max_val = max(max(affinity)); affinity = affinity ./ max_val; for i = 1 : size(affinity,1) affinity(i,i) = 1; end %sigal 29.10.13 - end affinity = sparse(affinity); end