Personalized page rank

include/boost/graph/personalized_page_rank.hpp

@@ -0,0 +1,236 @@
+// Copyright 2026 Emmanouil Krasanakis
+
+// Distributed under the Boost Software License, Version 1.0.
+// (See accompanying file LICENSE_1_0.txt or copy at
+// http://www.boost.org/LICENSE_1_0.txt)
+
+//  Authors: Emmanouil Krasanakis
+
+#ifndef BOOST_GRAPH_PERSONALIZED_PAGE_RANK_HPP
+#define BOOST_GRAPH_PERSONALIZED_PAGE_RANK_HPP
+
+#include <boost/property_map/property_map.hpp>
+#include <boost/graph/graph_traits.hpp>
+#include <boost/graph/properties.hpp>
+#include <boost/graph/iteration_macros.hpp>
+#include <boost/graph/overloading.hpp>
+#include <boost/graph/detail/mpi_include.hpp>
+#include <boost/property_map/function_property_map.hpp>
+#include <vector>
+
+namespace boost
+{
+namespace graph
+{
+    struct rank_convergence
+    {
+        explicit rank_convergence(std::size_t iters, double tol=0) : iters(iters), tol(tol) {} // allowing tolerance for early stopping
+        template < typename RankMap, typename RankMap2, typename Graph >
+        bool operator()(const RankMap& current, const RankMap2& previous, const Graph& g)
+        {
+            if (--iters == 0)
+                return true;
+            if (!tol)
+                return false;
+            using rank_type = typename property_traits< RankMap >::value_type;
+            rank_type sum_abs(0);
+            for (auto v : boost::make_iterator_range(vertices(g)))
+                sum_abs += std::abs(get(current, v) - get(previous, v));
+            return sum_abs*num_vertices(g)<tol;
+        }
+        std::size_t iters;
+        double tol;
+    };
+
+    namespace personalized_page_rank_detail
+    {
+        template <
+            typename Graph,
+            typename WeightMap,
+            typename PersonalizationMap,
+            typename RankMap,
+            typename RankMap2 >
+        void personalized_page_rank_step(
+            const Graph& g,
+            WeightMap weight_map,
+            PersonalizationMap personalization_map,
+            RankMap from_rank,
+            RankMap2 to_rank,
+            typename property_traits< RankMap >::value_type damping,
+            incidence_graph_tag)
+        {
+            using rank_type = typename property_traits< RankMap >::value_type;
+            rank_type l1_norm(0); // Computing the norm simultaneously avoids an extra summing iteration.
+
+            // Initialize the constant part of maps.
+            for (auto v : boost::make_iterator_range(vertices(g))) 
+            {
+                auto v_constant = rank_type(1 - damping) * get(personalization_map, v);
+                put(to_rank, v, v_constant);
+                l1_norm += v_constant;
+            }
+
+            // Accumulate from neighbors.
+            for (auto u : boost::make_iterator_range(vertices(g)))
+            {
+                rank_type u_rank_factor = damping * get(from_rank, u);
+                rank_type l1_accumulated_norm(0); // TBD: Consider making l1_norm volatile to reduce accumulation errors.
+                for (auto e : boost::make_iterator_range(out_edges(u, g))) 
+                {
+                    auto v = target(e, g);
+                    rank_type u_rank_out = get(weight_map, e)*u_rank_factor;
+                    put(to_rank, v, get(to_rank, v) + u_rank_out);
+                    l1_accumulated_norm += u_rank_out;
+                }
+                l1_norm += l1_accumulated_norm;
+            }
+            // If there are negative edge weights, or if negative damping is used, l1_norm could be zero or near-zero. 
+            // Division in those cases is conceptually correct for floating point weights, and actually expected behavior. 
+            // That said, such edge cases are impossible to arise for all typical algorithm uses.
+            for (auto v : boost::make_iterator_range(vertices(g))) 
+                put(to_rank, v, get(to_rank, v)/l1_norm); 
+        }
+
+        template <
+            typename Graph,
+            typename WeightMap,
+            typename PersonalizationMap,
+            typename RankMap,
+            typename RankMap2 >
+        void personalized_page_rank_step(
+            const Graph& g,
+            WeightMap weight_map,
+            PersonalizationMap personalization_map,
+            RankMap from_rank,
+            RankMap2 to_rank,
+            typename property_traits< RankMap >::value_type damping,
+            bidirectional_graph_tag)
+        {
+            using damping_type = typename property_traits< RankMap >::value_type;
+            damping_type l1_norm(0); // Computing the norm simultaneously avoids an extra summing iteration.
+            for (auto v : boost::make_iterator_range(vertices(g)))
+            {
+                damping_type rank(0);
+                for (auto e : boost::make_iterator_range(in_edges(v, g))) 
+                    rank += get(from_rank, source(e, g))*get(weight_map, e);
+                auto v_score = (damping_type(1) - damping) * get(personalization_map, v) + damping * rank;
+                put(to_rank, v, v_score);
+                l1_norm += v_score;
+            }
+            // See above function for potential division by zero comments.
+            for (auto v : boost::make_iterator_range(vertices(g))) 
+                put(to_rank, v, get(to_rank, v)/l1_norm);
+        }
+    } // end namespace personalized_page_rank_detail
+
+    template <
+        typename Graph,
+        typename WeightMap,
+        typename PersonalizationMap,
+        typename RankMap,
+        typename Done,
+        typename RankMap2 >
+    Done personalized_page_rank(
+        const Graph& g,
+        WeightMap weight_map,
+        PersonalizationMap personalization_map,
+        RankMap rank_map,
+        Done done,
+        typename property_traits< RankMap >::value_type damping,
+        RankMap2 rank_map2
+        BOOST_GRAPH_ENABLE_IF_MODELS_PARM(Graph, vertex_list_graph_tag))
+    {
+        using rank_type = typename property_traits< PersonalizationMap >::value_type;
+        rank_type personalization_norm(0);
+        for (auto v : boost::make_iterator_range(vertices(g))) 
+            personalization_norm += get(personalization_map, v);
+
+        // TBD: This implementation couples iterators when possible under reduced L1 cache invalidation assumptions,
+        // but this is not necessarily the case because we may be grabbing 2x memory lanes each time to write there.
+        // Could investigate which pattern is faster in the future.
+        for (auto v : boost::make_iterator_range(vertices(g)))
+        {
+            rank_type value = get(personalization_map, v)/personalization_norm;
+            put(personalization_map, v, value);
+            put(rank_map, v, value);
+        }
+
+        bool to_map_2 = true;
+        do
+        {
+            typedef typename graph_traits< Graph >::traversal_category category;
+            if (to_map_2)
+                personalized_page_rank_detail::personalized_page_rank_step(g, weight_map, personalization_map, rank_map, rank_map2, damping, category());
+            else
+                personalized_page_rank_detail::personalized_page_rank_step(g, weight_map, personalization_map, rank_map2, rank_map, damping, category());
+            to_map_2 = !to_map_2;
+        } 
+        while ((to_map_2  && !done(rank_map,  rank_map2, g)) || (!to_map_2 && !done(rank_map2, rank_map,  g))); // Done may not be symmetric.
+
+        // Now multiply the result with personalization_norm to restore the order of magnitude and store it in rank_map.
+        // Also restore the original personalization_map's magnitude for reuse (this is lossy up to numerical tolerance 
+        // but leaner than making a copy).
+        if (!to_map_2)
+        {
+            for (auto v : boost::make_iterator_range(vertices(g)))
+            {
+                put(rank_map, v, get(rank_map2, v)*personalization_norm);
+                put(personalization_map, v, get(personalization_map, v)*personalization_norm);
+            }
+        }
+        else
+        {
+            for (auto v : boost::make_iterator_range(vertices(g)))
+            {
+                put(rank_map, v, get(rank_map, v)*personalization_norm);
+                put(personalization_map, v, get(personalization_map, v)*personalization_norm);
+            }
+        }
+        return done;
+    }
+
+    template <
+        typename Graph,
+        typename WeightMap,
+        typename PersonalizationMap,
+        typename RankMap,
+        typename Done >
+    Done personalized_page_rank(
+        const Graph& g,
+        WeightMap weight_map,
+        PersonalizationMap personalization_map,
+        RankMap rank_map,
+        Done done,
+        typename property_traits< RankMap >::value_type damping)
+    {
+        using rank_type = typename property_traits< RankMap >::value_type;
+        std::vector< rank_type > ranks2(num_vertices(g));
+        return personalized_page_rank(g, weight_map, personalization_map, rank_map, done, damping,
+            make_iterator_property_map(ranks2.begin(), get(vertex_index, g)));
+    }
+
+    template < typename Graph, typename PersonalizationMap, typename RankMap >
+    rank_convergence personalized_page_rank(
+        const Graph& g,
+        PersonalizationMap personalization_map,
+        RankMap rank_map,
+        typename property_traits< RankMap >::value_type damping=0.85)
+    {
+        // This is the most traditional personalized PageRank implementation, with minimized signature.
+        using Edge = typename graph_traits<Graph>::edge_descriptor;
+        using rank_type = typename property_traits< RankMap >::value_type;
+        std::vector< rank_type > ranks2(num_vertices(g));
+        auto markovian_weights = make_function_property_map<Edge, double>([&g](Edge e){ return 1.0 / out_degree(source(e, g), g); });
+        return personalized_page_rank(g,
+            markovian_weights, 
+            personalization_map, 
+            rank_map, 
+            rank_convergence(100, 1.E-9), 
+            damping,
+            make_iterator_property_map(ranks2.begin(), get(vertex_index, g)));
+    }
+
+}
+} // end namespace boost::graph
+
+#endif // BOOST_GRAPH_PERSONALIZED_PAGE_RANK_HPP

test/Jamfile.v2

@@ -167,6 +167,7 @@ alias graph_test_regular :
     [ run delete_edge.cpp ]
     [ run johnson-test.cpp ]
     [ run lvalue_pmap.cpp ]
+    [ run personalized_pagerank_test.cpp ]
     ;
 
 alias graph_test_with_filesystem : :

test/personalized_pagerank_test.cpp

@@ -0,0 +1,71 @@
+
+#include <boost/graph/adjacency_list.hpp>
+#include <boost/graph/personalized_page_rank.hpp>
+#include <boost/property_map/property_map.hpp>
+#include <iostream>
+#include <vector>
+#include <iomanip>
+
+int main(int, char*[]) {
+    using namespace boost;
+    // deliberately hard (slow-converging) graph
+    using Graph = adjacency_list<vecS, vecS, directedS>;
+    std::vector<std::pair<int,int>> edges = {
+        {0,1},{1,0},{1,2},{2,1},{2,3},{3,2},
+        {4,5},{5,4},{5,6},{6,5},{6,7},{7,6},{7,8},{8,7},{8,9},{9,8},{9,10},{10,9},
+        {0,3},{3,0},{1,3},{3,1},{1,4},{4,1},
+        {4,6},{6,4},{6,9},{9,6},{6,8},{8,6},{7,9},{9,7},{8,10},{10,8},
+        {11,10},{10,11},{10,12},{12,10}
+    };
+    Graph g(edges.begin(), edges.end(), 13);
+
+    std::vector<double> ranks(num_vertices(g));
+    auto rank_map = make_iterator_property_map(ranks.begin(), get(vertex_index, g));
+    std::vector<double> personalization(num_vertices(g));
+    auto personalization_map = make_iterator_property_map(personalization.begin(), get(vertex_index, g));
+    personalization[0] = 1;
+    personalization[1] = 1;
+    personalization[2] = 1;
+    personalization[3] = 1;
+
+    std::size_t max_iters(100); // Convergence is so bad in this graph that it needs such a high cap.
+    using Edge = graph_traits<Graph>::edge_descriptor;
+    auto weight = make_function_property_map<Edge, double>(
+        [&g](Edge e){ return 1.0 / std::sqrt(double(out_degree(source(e, g), g) * out_degree(target(e, g), g))); });
+    auto convergence1 = graph::rank_convergence(max_iters, 1.E-9);
+    convergence1 = graph::personalized_page_rank(g, weight, personalization_map, rank_map, convergence1, 0.9);
+
+    std::cout << "ended after "<<max_iters-convergence1.iters << " iterations\n";
+    std::cout << std::fixed << std::setprecision(4);
+    for (std::size_t v = 0; v < num_vertices(g); ++v) 
+    {
+        std::cout << "vertex " << v << "  rank=" << ranks[v] << "\n";
+    }
+    BOOST_ASSERT(ranks[0]==ranks[2]); // should be exactly equal due to symmetry, even when considering floating point coarseness
+    BOOST_ASSERT(ranks[0]<ranks[1]+0.1);
+    BOOST_ASSERT(ranks[8]==ranks[9]);
+    BOOST_ASSERT(ranks[11]==ranks[12]);
+    BOOST_ASSERT(ranks[11]>0);
+    BOOST_ASSERT(convergence1.iters<max_iters);
+
+    // COMPUTING A HIGH-PASS VERSION BY PASSING NEGATIVE DAMPING (resulting values can be negative)
+    // This is not completely correct yet for computing graph gradients, because it needs to eskew normalization,
+    // so we need to be able to customize said normalization.
+    // Damping should generaly be in the range [-1,1].
+    auto renorm_weight = make_function_property_map<Edge, double>(
+        [&g](Edge e){ return 1.0 / std::sqrt(double((1.0+out_degree(source(e, g), g)) * (1.0+out_degree(target(e, g), g)))); });
+    auto convergence2 = graph::rank_convergence(max_iters, 1.E-9);
+    convergence2 = graph::personalized_page_rank(g, renorm_weight, personalization_map, rank_map, convergence2, -0.8);
+    std::cout << "ended after "<<max_iters-convergence2.iters << " iterations\n";
+    std::cout << std::fixed << std::setprecision(4);
+    for (std::size_t v = 0; v < num_vertices(g); ++v) 
+    {
+        std::cout << "vertex " << v << "  flow=" << ranks[v] << "\n";
+    }
+    BOOST_ASSERT(ranks[0]==ranks[2]); // should be exactly equal due to symmetry, even when considering floating point coarseness
+    BOOST_ASSERT(ranks[0]>ranks[1]+0.1);
+    BOOST_ASSERT(ranks[8]==ranks[9]);
+    BOOST_ASSERT(ranks[11]==ranks[12]);
+    BOOST_ASSERT(ranks[11]<0);
+    BOOST_ASSERT(convergence2.iters<max_iters);
+}