Why my code is very slow?

hello,

Can anybody optimize my code to be faster.

It is very slowly, in addition to that the network I used is very large.

I badly need to optimize it. My code include whil and several for that is why it is slow, and I does not know how upgrade it.

Thanks in advance

function diff =IC_realdata1(node)
s1=xlsread('d:\retweet.csv');
s2=s1(1:1000,:);
uu=unique(s2(:,2));
%   vec(:,1)=(1:length(uu));
%   vec(:,2)=uu;
g=dlmread('d:\sample\rel1.txt');
g=g(1:1000,:);

a0=node;%randi(length(uu),1);
T=false;
a1=a0;
b=a0;neb2=[];
while ~ T
         neb1=[];
    for i=1:length(b)
                y=g(b(i),:);
        nb=y(y~=0);
                neb=[];
        kk=1;
        for j=1:length(nb)
                        idx=find(uu==nb(j));%% to be sure if distination within source
            if ~isempty(idx)
                neb(kk)=idx; %vec(idx,1);
                kk=kk+1;
            end
                  end
               if ~isempty(neb)
            neb1=[neb1 neb];
                        for k=1:length(neb)
                r=ismember (neb(k),a1);
                if r==1
                                        neb1(neb1==neb(k))=[];
                                    end
            end
            neb2= [neb2 neb1];
        else
            neb2=[];
        end
    end
        neb=neb2;
    neb=unique(neb);
    for j=1:length(a0)
        neb(neb==a0(j))=[];
    end
    b=neb1;
    a1=union(a1,b);
        if isempty(b)
        T= true;
            end
end
diff=a1;

[Walter Roberson]

Do not expect people to analyze your code in detail to figure out what it is intended to do and then invent better ways of doing it. You should document the purpose of the code, and have comments about how the code achieves the purpose.

[huda nawaf]

Thanks for notes

The purpose of the code is to find the cascade of each node using the independent cascade model. It means when inputting the id of a node that tweeted the function must return all infected (interact) nodes with that tweet

function diff =IC_realdata1(node)  %%% the input of the function is the id of node(seed)
s1=xlsread('d:\retweet.csv'); %% network of  twitter (include two colomns; tweet and retweet)
uu=unique(s2(:,2));   %%% extract only the source node ( who tweeted )
g=dlmread('d:\sample\rel1.txt');  %%% read the relations file ( each node with their relations, I created it from s1)
a0=node;%% initial node (active or seed node), Sometimes a0 contains more than one node (for simplicity we make it one )
T=false; %% condition
a1=a0; %%% a1 is the vector save all infected nodes including the initial one
b=a0;neb2=[];
while ~ T %%% the code continue in execution  as long as b is not empty( b is a vector including the current nodes that not have been processed yet) 
    neb1=[];
    for i=1:length(b)
        y=g(b(i),:); %% extract the neighbors (relations in g)of current node 
        nb=y(y~=0); %% 
        neb=[];
        kk=1;
        for j=1:length(nb)  %% for each neighbor
            idx=find(uu==nb(j));%%  extract the index of each neighbor in uu 
            if ~isempty(idx) %% to be sure if the neighbor within source nodes or not 
                neb(kk)=idx; % %% if it is, it should save it in neb, then track each one and their relations  
                kk=kk+1;
            end
        end
        if ~isempty(neb)
            neb1=[neb1 neb]; %%% we need a buffer to save the nodes in neb because neb will subject to change 
            for k=1:length(neb)
                r=ismember (neb(k),a1); %%to check if any node in neb within a1
                if r==1
                    neb1(neb1==neb(k))=[]; %%if it is, it should remove it from neb1 ( we need neb1 include the new infected nodes)                    infected nodes)
                    end
                end
                neb2= [neb2 neb1];  %%we need another buffer neb2
            else  %% if neb is empty
                neb2=[]; %% it should make neb2 is empty
            end
        end (end if (neb));
            %%%% after finish for (b), it means complete all nodes in it
            neb=neb2;  %% make ned= neb2 whether neb2 is empty or has nodes
            neb=unique(neb);
            for j=1:length(a0)
                neb(neb==a0(j))=[]; %% remove from neb the initial node(s) that exists at the same time in a0 
            end
            b=neb1; %% to make b include the current nodes that have not been processed yet
            a1=union(a1,b); %% we  unify a1 with b, because a1 has to include all infected nodes
            if isempty(b) %% if b is empty that means there are no any longer infected nodes, then while will stop
                T= true;
            end
        end (end of while)
            diff=a1;