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MoleTrust.java
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MoleTrust.java
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package happy.research.utils;
import java.util.ArrayList;
import java.util.HashMap;
import java.util.List;
import java.util.Map;
/**
*
* @author guoguibing
*
*/
public class MoleTrust
{
/**
* Implementing MoleTrust Local Trust Metric: based on Epinions data set
*
* @param trust_data
* @param sourceUser
* @param horizon
* @return map {trusted neighbour - trust score} , if no trusted neighbour, then return empty map
*/
@SuppressWarnings("unchecked")
public static Map<Integer, Double> runAlgorithm(Map<Integer, Map<Integer, Double>> trust_data, Integer sourceUser,
int horizon)
{
// all the visited nodes
List<Integer> nodes = new ArrayList<>(40163);
// source user - edges[target users - trust value]
Map<Integer, Map<Integer, Double>> edges = new HashMap<>(40163);
/* Step 1: construct directed graphic and remove cyclic */
int dist = 0;
List<Integer>[] users = new List[horizon + 1];
users[dist] = new ArrayList<>();
users[dist].add(sourceUser);
nodes.add(sourceUser);
// Denote su: source user; tu: target user
while (dist < horizon)
{
dist++;
users[dist] = new ArrayList<>();
for (Integer su : users[dist - 1])
{
Map<Integer, Double> tns = trust_data.get(su);
if (tns == null) continue; // no trusted neighbours
for (Integer tn : tns.keySet())
{
if (!nodes.contains(tn) && !users[dist].contains(tn) && !users[dist - 1].contains(tn))
{
users[dist].add(tn);
}
}
}
for (Integer su : users[dist - 1])
{
Map<Integer, Double> tns = trust_data.get(su);
if (tns == null) continue;
for (Integer tu : tns.keySet())
{
if (!nodes.contains(tu) && users[dist].contains(tu))
{
Map<Integer, Double> tuTrusts;
if (edges.containsKey(su)) tuTrusts = edges.get(su);
else tuTrusts = new HashMap<>();
double trustValue = tns.get(tu);
tuTrusts.put(tu, trustValue);
edges.put(su, tuTrusts);
}
}
}
}
/* Step 2: Evaluate trust score */
dist = 0;
double threashold = 0.5;
// trusted neighbours - trust score map
Map<Integer, Double> trustScores = new HashMap<>();
trustScores.put(sourceUser, 1.0);
while (dist < horizon)
{
dist++;
for (Integer tu : users[dist])
{
double sum = 0.0;
double weights = 0.0;
for (Integer su : users[dist - 1])
{
Map<Integer, Double> tuTrusts = edges.get(su);
if (tuTrusts == null) continue; // no edges for user su
if (tuTrusts.containsKey(tu))
{
double trust_edge = tuTrusts.get(tu);
if (trust_edge > threashold)
{
sum += trust_edge * trustScores.get(su);
weights += trustScores.get(su);
}
}
}
double score = sum / weights;
trustScores.put(tu, score);
}
}
trustScores.remove(sourceUser);
return trustScores;
}
/**
* Implement MoleTrust on FilmTrust data set
*
* @param trust_data
* @param sourceUser
* @param horizon
* @return map {trusted neighbour - trust score} , if no trusted neighbour, then return empty map
*/
@SuppressWarnings("unchecked")
public static Map<String, Double> runAlgorithm(Map<String, Map<String, Double>> trust_data, String sourceUser,
int horizon)
{
// all the visited nodes
List<String> nodes = new ArrayList<>(1000);
// source user - edges[target users - trust value]
Map<String, Map<String, Double>> edges = new HashMap<>(1000);
/* Step 1: construct directed graphic and remove cyclic */
int dist = 0;
List<String>[] users = new List[horizon + 1];
users[dist] = new ArrayList<>();
users[dist].add(sourceUser);
nodes.add(sourceUser);
// Denote su: source user; tu: target user
while (dist < horizon)
{
dist++;
users[dist] = new ArrayList<>();
for (String su : users[dist - 1])
{
Map<String, Double> tns = trust_data.get(su);
if (tns == null) continue; // no trusted neighbours
for (String tn : tns.keySet())
{
if (!nodes.contains(tn) && !users[dist].contains(tn) && !users[dist - 1].contains(tn))
{
users[dist].add(tn);
}
}
}
for (String su : users[dist - 1])
{
Map<String, Double> tns = trust_data.get(su);
if (tns == null) continue;
for (String tu : tns.keySet())
{
if (!nodes.contains(tu) && users[dist].contains(tu))
{
Map<String, Double> tuTrusts;
if (edges.containsKey(su)) tuTrusts = edges.get(su);
else tuTrusts = new HashMap<>();
double trustValue = trust_data.get(su).get(tu);
tuTrusts.put(tu, trustValue);
edges.put(su, tuTrusts);
}
}
}
}
/* Step 2: Evaluate trust score */
dist = 0;
double threashold = 0.0;
// trusted neighbours - trust score map
Map<String, Double> trustScores = new HashMap<>();
trustScores.put(sourceUser, 1.0);
while (dist < horizon)
{
dist++;
for (String tu : users[dist])
{
double sum = 0.0;
double weights = 0.0;
for (String su : users[dist - 1])
{
Map<String, Double> tuTrusts = edges.get(su);
if (tuTrusts == null) continue; // no edges for user su
if (tuTrusts.containsKey(tu))
{
double trust_edge = tuTrusts.get(tu);
if (trust_edge > threashold)
{
sum += trust_edge * trustScores.get(su);
weights += trustScores.get(su);
}
}
}
double score = sum / weights;
trustScores.put(tu, score);
}
}
trustScores.remove(sourceUser);
return trustScores;
}
}