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- function [ affinity ] = CalculateAffinityByAdamicAdar_S3( data, actual_graph_size, num_missing_nodes, num_attr_nodes, attWeight, connectPHsToNeighbors, addMissingAtt)
- %UNTITLED2 adamic/adar(i,j) = sum( 1 / log(num_neighbors(k) | k is a
- %neighbor of both i and j
- % Detailed explanation goes here
-
- fprintf('CalculateAffinityByAdamicAdar_Sparse: NNZ(data) = %d\n', nnz(data));
- %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 connectPHsToNeighbors > 0
- data = ConnectMissingNodesToNeighborsNeighbors(data, actual_graph_size, num_missing_nodes, num_attr_nodes, addMissingAtt);
- end
- affinity = sparse(size(data,1), size(data,2));
-
- % Sigal 5.3.13: normalizeWeight
- % make sure that ratio att/link is same w/1-w
- if attWeight < 1 && attWeight >= 0
- normalizeWeight = attWeight/(1-attWeight);
- else
- normalizeWeight = 1;
- end
-
- numNodes = size(data,1);
-
- for k = 1 :numNodes
- %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 - 17.12.12 - find # social neighbors of k
- %sigal - update 23.3.13
- if num_attr_nodes>0
- neighbors_k_vec_social = data(k,:);
- neighbors_k_att = find(neighbors_k_vec_social(1:num_attr_nodes));
- neighbors_k_vec_social(1:num_attr_nodes)=0;
- neighbors_k_vec_social(k) = 1;
- %neighbors_k_social2 = find(neighbors_k_vec_social); %% temp !!!
- neighbors_k_social = num_attr_nodes+find(neighbors_k_vec_social(1+num_attr_nodes:end));
- num_neighbors_k_social = size(neighbors_k_social, 2);
- else
- num_neighbors_k_social = num_neighbors_k;
- end
-
-
- if num_neighbors_k_social > 1 && num_neighbors_k > 1
- if k>num_attr_nodes
- w = 1;
- else
- w = normalizeWeight;
- end
- inv_log_num_neighbors_k = w / log(num_neighbors_k);
- inv_log_num_neighbors_k_social = w / log(num_neighbors_k_social);
- if num_attr_nodes==0
- affinity(neighbors_k,neighbors_k) = affinity(neighbors_k,neighbors_k)+ inv_log_num_neighbors_k_social;
- else
- affinity(neighbors_k_social,neighbors_k_social) = affinity(neighbors_k_social,neighbors_k_social)+ inv_log_num_neighbors_k_social;
- if k>num_attr_nodes
- affinity(neighbors_k_att,neighbors_k_social) = affinity(neighbors_k_att,neighbors_k_social) + inv_log_num_neighbors_k;
- affinity(neighbors_k_social,neighbors_k_att) = affinity(neighbors_k_social,neighbors_k_att) + inv_log_num_neighbors_k;
- affinity(neighbors_k_att,neighbors_k_att) = affinity(neighbors_k_att,neighbors_k_att) + inv_log_num_neighbors_k;
- end
- end
- end
- end
-
- %normalize the matrix
- %affinity = affinity ./ max(max(affinity));
-
- %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
-
-
- %filter low values
- % thIn = 0.15;
- % thFactor = 100; %% 50*normalizeWeight
- % if num_attr_nodes>0
- % threshold = thIn/thFactor;
- % ind = affinity<threshold;
- % fprintf('CalculateAffinityByAdamicAdar_Sparse: NNZ(affinity) = %d (before threshold %.4f)\n', nnz(affinity), threshold);
- % affinity(ind) = 0;
- % end
-
- fprintf('CalculateAffinityByAdamicAdar_Sparse: NNZ(affinity) = %d\n', nnz(affinity));
-
- end
-
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