Absorbing Random Walk Centrality

A short introduction by example

The absorbing-centrality module contains an implementation for a greedy algorithm to compute the k-central nodes in a graph according to the Absorbing Random-Walk (ARW) centrality measure. The measure and the greedy algorithm are discussed in our paper that appeared in ICDM 2015. Here we provide a short description of the measure and the associated problem, taken from its abstract.

"Given a graph $G = (V,E)$ and a set of query nodes $Q\subseteq V$, we aim to identify the $k$ most central nodes in $G$ with respect to $Q$. Specifically, we consider central nodes to be absorbing for random walks that start at the query nodes $Q$. The goal is to find the set of $k$ central nodes that minimizes the expected length of a random walk until absorption."

This Python 3 notebook demonstrates usage of the greedy algorithm with a simple example.

Note: Run the Setup cells at the bottom of the notebook before other code cells.


For this example, we'll be using Zachary's Karate Club as our dataset. Let's load and draw its graph.

In [5]:
graph = nx.karate_club_graph() # load the graph
node_positions = nx.spring_layout(graph) # fix the node positions
make_graph_plot(graph, node_positions, node_size = 0,
                    node_color = "white", with_labels = True)

Optimizing Absorbing Random-Walk (ARW) Centrality

Now let's find the most central nodes according to ARW centrality. The problem takes the following parameters as input:

  • Query nodes Q: random walks in our model start from nodes in Q. In our examples here, we also assume that a random walk might start from each node in Q with equal probability.
  • Candidate nodes D: the set of nodes from which we select central nodes.
  • Restart parameter α: at each step, a random walk 'restarts' - i.e. moves to one of the query nodes in the next step - with probability (1-α), otherwise continues to an adjacent node.
  • The number of central nodes k: the number of candidate nodes that we wish to identify as central.

For our example, we have the following configuration:

In [6]:
Q = {5, 12, 14, 15, 26} # a small number of query nodes
D = graph.nodes() # all nodes are candidate nodes
alpha = 0.85 # the random walk restarts with
             # probability 1 - α = 0.15 in each step
k = 3 # number of central nodes

We employ a greedy algorithm, implemented as absorbing_centrality.algorithms.greedy_team, to select the k = 3 most central nodes. The algorithm performs k steps, and at each step select one additional node that improves ARW centrality the most.

In [7]:
result = arw.algorithms.greedy_team(graph, k, query = Q, candidates = D,
                           with_restarts = True, alpha = alpha)

The algorithm returns a tuple with the following two elements:

  • A list of centrality scores, one for each step of the algorithm.
  • A list of node-sets, one for each step of the algorithm.
In [8]:
centrality_scores, node_sets = result

The i-th element of centrality_scores is the centrality score obtained for the i-th set of central nodes built by greedy. Moreover, the i-th node-set is always a subset of the (i+1)-th node-set.

In [9]:
print("Greedy returned {0} centrality scores and {1} node-sets for k = {2}."\
      .format(len(centrality_scores), len(node_sets), k))
for i in range(k):
    print("Centrality score {0:.2f} for nodes {1}."
          .format(centrality_scores[i][0], node_sets[i]))
Greedy returned 3 centrality scores and 3 node-sets for k = 3.
Centrality score 6.09 for nodes [33].
Centrality score 2.91 for nodes [33, 0].
Centrality score 1.95 for nodes [33, 0, 32].

Notice that ARW centrality decreases as more nodes are selected by greedy. That behavior is expected because, with more nodes acting as absorbing, random walks are more likely to be absorbed earlier, i.e. have decreased absorption time -- thus leading to decreased ARW centrality, by definition.

The $k$ nodes selected by greedy are returned as the last node-set and are associated with the last returned centrality score.

In [10]:
central_nodes = node_sets[k-1]
solution_centrality = centrality_scores[k-1][0]
In [11]:
print("Greedy selected nodes {0} as central, with centrality score {1:.2f}."\
      .format(central_nodes, solution_centrality))
Greedy selected nodes [33, 0, 32] as central, with centrality score 1.95.

The plot below shows query nodes Q in red color and central nodes returned by greedy in blue.

In [12]:
query_node_params = NodeParams(Q, "red", 150)
central_node_params = NodeParams(central_nodes, "blue", 200)
node_color, node_size = set_node_color_and_size(graph,
                            query_node_params, central_node_params)
make_graph_plot(graph, node_positions, node_size, node_color, with_labels = False)

Is greedy optimal?

The greedy algorithm discussed above is easy to implement but generally does not return optimal solutions to the ARW problem. This is most easily demonstrated for the case where

  • all query nodes are also candidate nodes, i.e., $Q \subseteq D$, and
  • we seek the k most central nodes, with $k = |Q| > 1$.

In that case, the set of query nodes is an optimal solution with centrality equal to zero (0); how close does greedy come to that?

Let us consider the same setting as in the previous example, but this time ask greedy for the most central k = 5 nodes.

In [13]:
k = 5

All other parameters of the problem remain the same and we invoke greedy as before.

In [14]:
greedy_result = arw.algorithms.greedy_team(graph, k, query = Q, candidates = D,
                                       with_restarts = True, alpha = alpha)
greedy_centrality_scores, greedy_node_sets = greedy_result
greedy_central_nodes = greedy_node_sets[k-1] # the k central nodes found by greedy...
greedy_centrality = greedy_centrality_scores[k-1][0] # ... and their centrality

print("Greedy returned nodes {0} as central, with centrality score {1:.2f}."\
      .format(greedy_central_nodes, greedy_centrality))
Greedy returned nodes [33, 0, 32, 5, 12] as central, with centrality score 0.72.

Let us plot greedy's solution. The plot below shows in blue those nodes returned as central by greedy and in red the query nodes that were not selected as central.

In [15]:
query_node_params = NodeParams(Q, "red", 150)
central_node_params = NodeParams(greedy_central_nodes, "blue", 200)
node_color, node_size = set_node_color_and_size(graph,
                            query_node_params, central_node_params)
make_graph_plot(graph, node_positions, node_size, node_color, with_labels = False)

We notice that the $k = 5$ nodes selected by greedy are not the same as the set of $k = 5$ query nodes. It is easy to see that the set of $k = 5$ query nodes would be the optimal solution in this case, with centrality zero (0), as all random walks starting from them would be absorbed immediately.

In [16]:
optimal_centrality = arw.absorbing_centrality(graph, Q, query=Q,
                                              with_restarts=True, alpha=0.85)

print("Optimal centrality {0:.2f} achieved for query nodes {1}."\
      .format(optimal_centrality, Q))
print("Greedy centrality {0:.2f} achieved for nodes {1}."\
      .format(greedy_centrality, set(greedy_central_nodes)))
Optimal centrality 0.00 achieved for query nodes {26, 12, 5, 14, 15}.
Greedy centrality 0.72 achieved for nodes {0, 33, 32, 12, 5}.

How good is greedy?

Greedy misses the optimal solution, but it does not perform arbitrarily badly. Firstly, it is easy to see that greedy does find the optimal solution for $k = 1$. Moreover, we have the following guarantee for $k > 1$:

Let $m$ be the optimal ARW centrality for $k = 1$. Moreover, let $c_{greedy}$ be the centrality of the solution returned by greedy for a given $k > 1$ and $c_{opt}$ the optimal centrality for the same $k > 1$. Then, we have $$(m - c_{greedy}) \geq (1 - \frac{1}{e}) (m - c_{opt}).$$

In [17]:
m = greedy_centrality_scores[0][0]
c_greedy = greedy_centrality_scores[k - 1][0] # for k = 5
c_opt = optimal_centrality # for k = 5

To demonstrate visually what the aforementioned guarantee means, let us consider the plot below. The plot shows with blue dots the ARW centrality of the node-sets built by greedy along its $k$ steps. In addition, the plot shows with grey horizontal lines the levels of $m$, $c_{greedy}$, and $c_{opt}$.

In [18]:
make_approximation_plot(m, c_greedy, c_opt, greedy_centrality_scores)

The length of the red segment in the plot is equal to $(m - c_{greedy})$. Intuitively, it the improvement in ARW centrality achieved by greedy as it builds node-sets of size $1$ to $k$.

Similarly, the length of the green segment is equal to $(m - c_{opt})$. Intuitively, it is the improvement in the optimal ARW centrality between node-sets of size $1$ to $k$.

The aforementioned guarantee means that the length of the red segment (improvement by greedy) will be at at least $(1 - \frac{1}{e}) \approx 0.63$ times the length of the green segment (optimal improvement). We can confirm numerically that it holds for this example.

In [19]:
question = lambda x, y: "Yes" if x >= (1 - 1/np.e) * y else "No"
answer = question(m - c_greedy, m - c_opt)
print("Does the inequality hold? -{0}".format(answer))
Does the inequality hold? -Yes


Run these cells before other code cells.

In [1]:
import sys
import networkx as nx
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import collections
In [2]:
import absorbing_centrality as arw
In [3]:
%matplotlib inline
In [4]:
NodeParams = collections.namedtuple("NodeParams", ['nodes', 'color', 'size'])

def set_node_color_and_size(graph, query_node_params, 
                            default_color = "grey", 
                            default_node_size = 30):
    Return a list of colors for the graph nodes.

    inverse_node_index = dict([(node, pos) for pos, node in enumerate(graph.nodes())])
    # initialize all nodes with default color
    node_color = len(graph) * [default_color]
    node_size = len(graph) * [default_node_size]
    for node_set_params in [query_node_params, candidate_node_params]:
        if node_set_params:
            for node in node_set_params.nodes:
                pos = inverse_node_index[node]
                node_color[pos] = node_set_params.color
                node_size[pos] = node_set_params.size
    return node_color, node_size

def make_graph_plot(graph, node_positions, node_size,
                                node_color, with_labels):
    Plot a networkx graph with custom node positions,
    sizes, color, and the option to have labels or not.
    fig, ax = plt.subplots(1, 1, figsize = (12, 12))
    nx.draw(graph, ax = ax, with_labels = with_labels, font_size = 20,
            node_size = node_size, node_color = node_color,
            edge_color = "grey", pos = node_positions)
def make_approximation_plot(m, c_greedy, c_opt, greedy_centrality_scores):
    Make a plot to demonstrate the approximation guarantee
    for the greedy algorithm.
    fig, ax = plt.subplots(1, 1, figsize = (10, 6))

    x = range(1, 1+k)
    y = [s[0] for s in greedy_centrality_scores]
    ax.set(xlim = (0.5, k+0.5), ylim = (-0.5, max(y) * 1.2))
    ax.set_xlabel("k", fontsize = 18)
    ax.set_ylabel("ARW centrality", fontsize = 18)
    ax.set(yticks = [m, c_greedy, c_opt],
           yticklabels = ["m = {0:.2f}".format(m),
                          "c_greedy = {0:.2f}".format(c_greedy),
                          "c_opt = {0:.2f}".format(c_opt)])

    _tmp = ax.plot((0, k+1), (y[0], y[0]), color = "grey", lw = 3)
    _tmp = ax.plot((0, k+1), (y[-1], y[-1]), color = "grey", lw = 3)
    _tmp = ax.plot((0, k+1), (0, 0), color = "grey", lw = 3)

    _tmp = ax.plot((1.25, 1.25), (y[0], y[-1]), lw = 5, color = "red",
                   alpha = 0.7, label = "m - c_greedy")
    _tmp = ax.plot((1.75, 1.75), (y[0], 0), lw = 5, color = "green", label = "m - c_opt")

    _tmp = ax.scatter(x, y, s = 150, lw =2, label = "greedy centrality")

    _tmp = ax.legend(loc = 1)