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realfolkcode · Apr 26, 2023 · 853bd7c · 853bd7c
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diff --git a/.gitignore b/.gitignore
diff --git a/LICENSE b/LICENSE
diff --git a/README.md b/README.md
@@ -0,0 +1,6 @@
+## Fast Approximate Convex Hull Construction in Networks via Node Embedding
+
+## Authors
+
+* Dmitrii Gavrilev
+* Ilya Makarov
diff --git a/batched_training.py b/batched_training.py
@@ -0,0 +1,67 @@
+import torch
+import dgl
+
+
+def loss_dist(X, X_nograd, A, idx, norm=1):
+    dist = torch.cdist(X, X_nograd, p=norm)
+    k = X_nograd.shape[0] * len(idx) - len(idx)
+    s = torch.sum((dist - A[idx])**2) / k
+    return s
+
+
+def loss_log_dist(X, X_nograd, A, idx, norm=1):
+    dist = torch.cdist(X, X_nograd, p=norm)
+    k = X_nograd.shape[0] * len(idx) - len(idx)
+    s = torch.sum((torch.log(dist + 1e-9) - torch.log(A[idx] + 1e-9))**2) / k
+    return s
+
+
+def l1_loss_dist(X, X_nograd, A, idx, norm=1):
+    dist = torch.cdist(X, X_nograd, p=norm)
+    k = X_nograd.shape[0] * len(idx) - len(idx)
+    s = torch.sum(torch.abs(dist - A[idx])) / k
+    return s
+
+
+def train(model, num_epochs, g, dist, loader, opt, scheduler, log_loss=False, max_grad_norm=2., verbose=1):
+    model.eval()
+    with torch.no_grad():
+        mfgs = [dgl.to_block(g) for _ in range(len(model.conv))]
+        inputs = mfgs[0].srcdata['x']
+        emb_nograd = model(mfgs, inputs)
+
+    for epoch in range(num_epochs):
+        model.train()
+        epoch_loss = 0
+        for input_nodes, output_nodes, mfgs in loader:
+            inputs = mfgs[0].srcdata['x']
+            emb = model(mfgs, inputs)
+            emb_nograd[output_nodes] = emb.detach()
+            if log_loss:
+                loss = loss_log_dist(emb, emb_nograd, dist, output_nodes)
+            else:
+                loss = loss_dist(emb, emb_nograd, dist, output_nodes)
+            opt.zero_grad()
+            loss.backward()
+            torch.nn.utils.clip_grad_norm_(model.parameters(), max_grad_norm)
+            opt.step()
+            if scheduler is not None:
+                scheduler.step()
+            epoch_loss += loss.item()
+        epoch_loss /= len(loader)
+        if epoch % verbose == 0:
+            print(f'Epoch: {epoch}, loss: {epoch_loss}')
+
+    model.eval()
+    with torch.no_grad():
+        mfgs = [dgl.to_block(g) for _ in range(len(model.conv))]
+        inputs = mfgs[0].srcdata['x']
+        emb = model(mfgs, inputs)
+    loss = loss_dist(emb, emb, dist, g.nodes())
+    print(f'Final loss: {loss.item()}')
+    loss = loss_log_dist(emb, emb, dist, g.nodes())
+    print(f'Final loss (log): {loss.item()}')
+    J = l1_loss_dist(emb, emb, dist, g.nodes())
+    print(f'Absolute loss (J): {J}')
+
+    return emb
diff --git a/convexity/algo.cpp b/convexity/algo.cpp
@@ -0,0 +1,152 @@
+#define _CRT_SECURE_NO_WARNINGS
+#include <vector>
+#include <deque>
+#include <fstream>
+#include <cstdio>
+#include <utility>
+#include <unordered_set>
+#include <random>
+#include <cassert>
+#include <numeric>
+#include <iostream>
+#include <chrono>
+#include <cstring>
+#include <climits>
+#include <iterator>
+#include <set>
+#include "algo.h"
+
+using namespace std;
+#define NO_VALUE -1
+
+SubGraph::SubGraph(const vector<vector<int>>& network) :
+	present(vector<char>(network.size()))
+{}
+inline bool SubGraph::insert(int vertex) {
+	bool inserted = !present[vertex];
+	if (inserted) {
+		present[vertex] = 1;
+		list.push_back(vertex);
+	}
+	return inserted;
+}
+
+template <typename T>
+bool contains(const vector<T>& vec, T& el) {
+	for (const T& i : vec) {
+		if (i == el) {
+			return true;
+		}
+	}
+	return false;
+}
+
+vector<vector<int>> readPajek(string fn, vector<string>* names) {
+	ifstream input(fn, ifstream::in);
+	if (!input.is_open()) {
+		perror(fn.c_str());
+		exit(0);
+	}
+	vector<vector<int>> res;
+	char line[1024];
+	input.getline(line, 1024);
+	int n = 0;
+	for (; ; n++) {
+		input.getline(line, 1024);
+		if (line[0] == '*') {
+			break;
+		}
+		if (names != nullptr) {
+			names->push_back(line);
+		}
+	}
+	res.resize(n, vector<int>());
+	int m = 0;
+	int a, b;
+	for (; ; m++) {
+		input.getline(line, 256);
+		if (sscanf(line, "%d %d", &a, &b) == EOF) {
+			break;
+		}
+		a--; // pajek uses 1-based indexing
+		b--;
+		if (!contains(res[a], b)) {
+			res[a].push_back(b);
+		}
+		if (!contains(res[b], a)) {
+			res[b].push_back(a);
+		}
+	}
+	return res;
+}
+
+vector<vector<int>> distances(const vector<vector<int>>& network) {
+	vector<vector<int>> res(network.size());
+	//res.reserve(network.size());
+#pragma omp parallel for
+	for (int vertex = 0; vertex < network.size(); vertex++) {
+		vector<int> distances(network.size(), NO_VALUE);
+		distances[vertex] = 0;
+		deque<int> todo;
+		todo.push_back(vertex);
+		while (!todo.empty()) {
+			int current = todo.front();
+			todo.pop_front();
+			for (int neighbor : network[current]) {
+				if (distances[neighbor] == NO_VALUE) {
+					distances[neighbor] = distances[current] + 1;
+					todo.push_back(neighbor);
+				}
+			}
+		}
+		res[vertex] = move(distances);
+	}
+	return res;
+}
+
+vector<int> convexHull(const vector<vector<int>>& network, const vector<vector<int>>& distances, SubGraph& subGraph, vector<int> base) {
+	deque<int> todo;
+	vector<int> insertions;
+	for (auto newVertex : base) {
+		todo.push_back(newVertex);
+		insertions.push_back(newVertex);
+		subGraph.insert(newVertex);
+	}
+	while (!todo.empty()) {
+		int current = todo.front();
+		todo.pop_front();
+		for (int neighbor : network[current]) {
+			if (!subGraph.present[neighbor]) {
+				for (int endVertex : subGraph.list) {
+					if (distances[current][endVertex] >= distances[current][neighbor] + distances[neighbor][endVertex]) {
+						todo.push_back(neighbor);
+						insertions.push_back(neighbor);
+						subGraph.insert(neighbor);
+						break;
+					}
+					else {
+					}
+				}
+			}
+		}
+	}
+
+	return insertions;
+}
+
+vector<vector<int>> generateBases(string fn, int k, int repeats) {
+	auto net = readPajek(fn);
+	vector<vector<int>> bases(repeats);
+	vector<int> nodes(net.size());
+	long long rnd_init = 14994518116208229;
+	std::default_random_engine generator(rnd_init);
+	for (int i = 0; i < net.size(); ++i) {
+		nodes[i] = i;
+	}
+	for (int i = 0; i < repeats; ++i) {
+		vector<int> s;
+		std::sample(nodes.begin(), nodes.end(), std::back_inserter(s), k, generator);
+		bases[i] = s;
+	}
+	return bases;
+}
diff --git a/convexity/algo.h b/convexity/algo.h
@@ -0,0 +1,36 @@
+#pragma once
+#define _CRT_SECURE_NO_WARNINGS
+#include <vector>
+#include <deque>
+#include <fstream>
+#include <cstdio>
+#include <utility>
+#include <unordered_set>
+#include <random>
+#include <cassert>
+#include <numeric>
+#include <iostream>
+#include <chrono>
+#include <cstring>
+
+using namespace std;
+#define NO_VALUE -1
+
+class SubGraph {
+public:
+	vector<char> present;
+	vector<int> list;
+	SubGraph(const vector<vector<int>>& network);
+	bool insert(int vertex);
+};
+
+template <typename T>
+bool contains(const vector<T>& vec, T& el);
+
+vector<vector<int>> readPajek(string fn, vector<string>* names = nullptr);
+
+vector<vector<int>> distances(const vector<vector<int>>& network);
+
+vector<int> convexHull(const vector<vector<int>>& network, const vector<vector<int>>& distances, SubGraph& subGraph, vector<int> base);
+
+vector<vector<int>> generateBases(string fn, int k, int repeats);
diff --git a/convexity/cli.cpp b/convexity/cli.cpp
@@ -0,0 +1,78 @@
+#include "algo.h"
+
+using namespace std;
+
+int main(int argc, char* argv[]) {
+	string fn_in;
+	string fn_out = "out.txt";
+	string fn_dist;
+	int k;
+	int repeats = 10;
+	//parse args
+	if (argc > 1) {
+		fn_in = argv[1];
+		cout << fn_in << endl;
+		if (argc > 2) {
+			sscanf(argv[2], "%d", &k);
+			cout << "k = " << k << endl;
+			if (argc > 3) {
+				sscanf(argv[3], "%d", &repeats);
+				cout << "repeats = " << repeats << endl;
+				if (argc > 4) {
+					fn_out = argv[4];
+					cout << fn_out << endl;
+				}
+			}
+		}
+		else {
+			cout << "Second argument should be number of elements!" << endl;
+		}
+	}
+	else {
+		cout << "First argument should be path to pajek file containing a network!" << endl;
+		exit(0);
+	}
+
+	auto net = readPajek(fn_in);
+	cout << "Network loaded." << endl;
+	cout << "Nodes in the largest connected component: " << net.size() << endl;
+
+	fn_dist = fn_out + ".dist";
+	fn_out = fn_out + ".out";
+	std::ofstream output(fn_out);
+	output << repeats << endl;
+
+	vector<vector<int>> bases = generateBases(fn_in, k, repeats);
+	for (auto b : bases) {
+		for (auto elem : b) {
+			output << elem << ' ';
+		}
+		output << endl;
+	}
+	cout << "Bases have been sampled" << endl;
+
+	std::ofstream dist_out(fn_dist);
+	auto dists = distances(net);
+	for (int i = 0; i < dists.size(); ++i) {
+		for (int j = 0; j < dists[i].size(); ++j) {
+			dist_out << dists[i][j] << " ";
+		}
+		dist_out << endl;
+	}
+	cout << "Distances have been calculated" << endl;
+
+	int i = 0;
+	for (auto b : bases) {
+		SubGraph s(net);
+		auto hull = convexHull(net, dists, s, b);
+		for (auto elem : hull) {
+			output << elem << ' ';
+		}
+		output << endl;
+		cout << "Hull: " <<i << endl;
+		++i;
+	}
+
+	output.close();
+	return 0;
+}
diff --git a/convexity/karate_club.dist b/convexity/karate_club.dist
@@ -0,0 +1,34 @@
+0 1 1 1 1 1 1 1 1 2 1 1 1 1 3 3 2 1 3 1 3 1 3 3 2 2 3 2 2 3 2 1 2 2 
+1 0 1 1 2 2 2 1 2 2 2 2 2 1 3 3 3 1 3 1 3 1 3 3 3 3 3 2 2 3 1 2 2 2 
+1 1 0 1 2 2 2 1 1 1 2 2 2 1 2 2 3 2 2 2 2 2 2 2 2 3 3 1 1 2 2 2 1 2 
+1 1 1 0 2 2 2 1 2 2 2 2 1 1 3 3 3 2 3 2 3 2 3 3 3 3 3 2 2 3 2 2 2 2 
+1 2 2 2 0 2 1 2 2 3 1 2 2 2 4 4 2 2 4 2 4 2 4 4 3 3 4 3 3 4 3 2 3 3 
+1 2 2 2 2 0 1 2 2 3 1 2 2 2 4 4 1 2 4 2 4 2 4 4 3 3 4 3 3 4 3 2 3 3 
+1 2 2 2 1 1 0 2 2 3 2 2 2 2 4 4 1 2 4 2 4 2 4 4 3 3 4 3 3 4 3 2 3 3 
+1 1 1 1 2 2 2 0 2 2 2 2 2 2 3 3 3 2 3 2 3 2 3 3 3 3 4 2 2 3 2 2 2 3 
+1 2 1 2 2 2 2 2 0 2 2 2 2 2 2 2 3 2 2 2 2 2 2 2 3 3 2 2 2 2 1 2 1 1 
+2 2 1 2 3 3 3 2 2 0 3 3 3 2 2 2 4 3 2 2 2 3 2 2 3 3 2 2 2 2 2 2 2 1 
+1 2 2 2 1 1 2 2 2 3 0 2 2 2 4 4 2 2 4 2 4 2 4 4 3 3 4 3 3 4 3 2 3 3 
+1 2 2 2 2 2 2 2 2 3 2 0 2 2 4 4 3 2 4 2 4 2 4 4 3 3 4 3 3 4 3 2 3 3 
+1 2 2 1 2 2 2 2 2 3 2 2 0 2 4 4 3 2 4 2 4 2 4 4 3 3 4 3 3 4 3 2 3 3 
+1 1 1 1 2 2 2 2 2 2 2 2 2 0 2 2 3 2 2 2 2 2 2 2 3 3 2 2 2 2 2 2 2 1 
+3 3 2 3 4 4 4 3 2 2 4 4 4 2 0 2 5 4 2 2 2 4 2 2 3 3 2 2 2 2 2 2 1 1 
+3 3 2 3 4 4 4 3 2 2 4 4 4 2 2 0 5 4 2 2 2 4 2 2 3 3 2 2 2 2 2 2 1 1 
+2 3 3 3 2 1 1 3 3 4 2 3 3 3 5 5 0 3 5 3 5 3 5 5 4 4 5 4 4 5 4 3 4 4 
+1 1 2 2 2 2 2 2 2 3 2 2 2 2 4 4 3 0 4 2 4 2 4 4 3 3 4 3 3 4 2 2 3 3 
+3 3 2 3 4 4 4 3 2 2 4 4 4 2 2 2 5 4 0 2 2 4 2 2 3 3 2 2 2 2 2 2 1 1 
+1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 2 0 2 2 2 2 3 3 2 2 2 2 2 2 2 1 
+3 3 2 3 4 4 4 3 2 2 4 4 4 2 2 2 5 4 2 2 0 4 2 2 3 3 2 2 2 2 2 2 1 1 
+1 1 2 2 2 2 2 2 2 3 2 2 2 2 4 4 3 2 4 2 4 0 4 4 3 3 4 3 3 4 2 2 3 3 
+3 3 2 3 4 4 4 3 2 2 4 4 4 2 2 2 5 4 2 2 2 4 0 2 3 3 2 2 2 2 2 2 1 1 
+3 3 2 3 4 4 4 3 2 2 4 4 4 2 2 2 5 4 2 2 2 4 2 0 2 1 2 1 2 1 2 2 1 1 
+2 3 2 3 3 3 3 3 3 3 3 3 3 3 3 3 4 3 3 3 3 3 3 2 0 1 3 1 2 3 3 1 2 2 
+2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 3 3 3 3 3 3 1 1 0 3 2 2 2 3 1 2 2 
+3 3 3 3 4 4 4 4 2 2 4 4 4 2 2 2 5 4 2 2 2 4 2 2 3 3 0 2 2 1 2 2 2 1 
+2 2 1 2 3 3 3 2 2 2 3 3 3 2 2 2 4 3 2 2 2 3 2 1 1 2 2 0 2 2 2 2 2 1 
+2 2 1 2 3 3 3 2 2 2 3 3 3 2 2 2 4 3 2 2 2 3 2 2 2 2 2 2 0 2 2 1 2 1 
+3 3 2 3 4 4 4 3 2 2 4 4 4 2 2 2 5 4 2 2 2 4 2 1 3 2 1 2 2 0 2 2 1 1 
+2 1 2 2 3 3 3 2 1 2 3 3 3 2 2 2 4 2 2 2 2 2 2 2 3 3 2 2 2 2 0 2 1 1 
+1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 3 2 2 2 2 2 2 2 1 1 2 2 1 2 2 0 1 1 
+2 2 1 2 3 3 3 2 1 2 3 3 3 2 1 1 4 3 1 2 1 3 1 1 2 2 2 2 2 1 1 1 0 1 
+2 2 2 2 3 3 3 3 1 1 3 3 3 1 1 1 4 3 1 1 1 3 1 1 2 2 1 1 1 1 1 1 1 0