Complex networks vulnerability to module-based attacks

Complex networks vulnerability to module-based attacks


In the multidisciplinary field of Network Science, optimization of procedures for efficiently breaking complex networks is attracting much attention from practical points of view. In this contribution we present a module-based method to efficiently break complex networks. The procedure first identifies the communities in which the network can be represented, then it deletes the nodes (edges) that connect different modules by its order in the betweenness centrality ranking list. We illustrate the method by applying it to various well known examples of social, infrastructure, and biological networks. We show that the proposed method always outperforms vertex (edge) attacks which are based on the ranking of node (edge) degree or centrality, with a huge gain in efficiency for some examples. Remarkably, for the US power grid, the present method breaks the original network of 4941 nodes to many fragments smaller than 197 nodes (4% of the original size) by removing mere 164 nodes ( 3%) identified by the procedure. By comparison, any degree or centrality based procedure, deleting the same amount of nodes, removes only 22% of the original network, i.e. more than 3800 nodes continue to be connected after that.

pacs:, 89.75.Fb

I Introduction

Network theory and its applications pervade many different scientific fields, from physics to sociology, engineering, epidemiology, mathematics and economy —to cite a few. In the context of network science, three important concepts have received much attention recently: interdependent graphs Buldyrev et al. (2010); Brummitt et al. (2012), communities (or modules) Fortunato (2010), and robustness of networks facing targeted attacks Valente (2012). In the present work we address and bring together these last two concepts.

The robustness of networks against failures, targeted attacks to individuals components, and the impact on the performance of the system has become an important issue for practical reasons in the last few years. In this sense, the robustness of a network is often related to the structural functionality of the system as a whole, so information can propagate over the network for example. For instance, the failure of routers in the Internet Cohen et al. (2001), the vaccination of individuals to prevent the spread of a disease Pastor-Satorras and Vespignani (2002), and fighting organized crime or terrorist groups Xu and Chen (2008) can all be described by a formal model in which a certain number of vertices (edges) in the network are removed Iyer et al. (2013). Therefore, the robustness of a complex network is directly related to the fraction of nodes (edges) needed to be removed so that the network loses its functionality.

Figure 1: Module based attack scheme: (A) represents the original network, (B) is one possible module representation of A, and (C) shows the internal structure of nodes and edges inside two selected modules and the edges connecting nodes between them; those edges (nodes) are the ones to be selected for deletion. The graph depicted in A corresponds the US power grid kon (2014a).

Conversely, the less the number of nodes that a method identifies to break down a network, the more efficient it is. In this sense, many centrality indexes have been proposed aimed to measure the structural importance of nodes (edges) Iyer et al. (2013). For instance, the concept of bridging nodes in the topology of complex networks has been brought to discussion too Valente and Fujimoto (2010). Hwang et al. Hwang et al. (2006) define a bridging centrality in order to characterize the location of nodes among high degree nodes. The method succeeds in identifying functional modules but does not show significantly better results than simple betweenness attack when it comes to atomize different complex networks. Nevertheless, recent developments in efficient community extraction algorithms from complex graphs Newman (2006); Blondel et al. (2008) show a promising pathway in devising better attack strategies. In effect, communities or modules are topological partitions of graphs with dense internal connections but weakly connected among them. In this sense, in Fig. 1 we depict the community structure for the Western United States Power Grid, illustrating this weak connection among clusters internally dense. Henceforth, a natural question arises: How structurally important are those weak interactions bridging distinct communities and how are they related to the robustness problem? To answer this query is precisely the main objective of this study. The work is organized as follows: section  II discuss the generalities of attack in networks and the concept of robustness, section  III describes our method to perform the attacks, while in section  IV we present the results of the procedure to ten examples of real networks with conclusions summarized in section  V.

Ii Attacks

In order to quantify the effect of the attacks on the networks Barthélemy (2011), we define as an initial network of size , and as the network that results after the removal of a fraction of vertices (edges). Then we denote by the largest component of , whose size we denote by . We define the order parameter = which allows us to quantify the response of a network as a function of the fraction of nodes (edges) deleted.

An hypothetical way of getting the ordered list of targeted nodes to be removed would be by brute force: try all the possible lists until find the one that reduce the network to a desired size with the minimum number of remotions. However this is useless because it means checking possible lists, which is computational prohibited for any network bigger than . On the other hand, the simplest but no efficient strategy is random deletion of nodes, i.e. make a random list of the nodes and remove them in that order. This generally gives rise to a linear degradation of the network in which the fraction removed is mostly not much than the nodes removed. A more efficient and doable way of attack a graph consists in the deletion of vertices (edges) in order of their importance in the structural functioning of the network. In this sense, traditional attacks focus on sorting nodes (edges) in decreasing order of some centrality index —the so called Centrality-Based Attack (CBA)—, which perform much better than random attacks. Betweenness centrality, for instance, takes a time, depending on the algorithm used, of only , where stands for the number of edges in the network. This way, if we choose some method as a null reference, the gain in efficiency can be computed by the normalized ratio


which increases as the attack method becomes more efficient than the reference one.

Even though most attack methods focus on centrality ranking, real networks tend to group into sparsely connected clusters and the removal of few bridging structures should be able to detach large chunks of densely connected nodes, leading to large values of , as we shall see in the next section.

Iii Module-based attack

The structural importance of a node (edge) depends both on local and non-local measures. Hence, in the scope of the method proposed in this paper, centrality and community detection are the topics that we address to characterize and sort nodes (edges) in order to develop the attack on networks. As pointed out in the works by Iyer et al. Iyer et al. (2013) and Holme et al. Holme et al. (2002) nodes with high betweenness and high degree are usually strongly correlated and both attacks have similar efficiency. Besides, previous work shows that for real networks the betweenness-based method is in general the most efficient Iyer et al. (2013). Thence, from now on we take betweenness centrality attack as our reference or null method.

Likewise, vertices connecting different communities generally have high betweenness centrality since many shortest paths pass through them. However, as fewer connections are expected among communities these nodes are not the ones with higher degree. Therefore, in order to detach communities in a very efficient way, we propose a Module-Based Attack (MBA) consisting of sorting all nodes (edges) by betweenness centrality, then choosing only those nodes (edges) that link different communities. One should note that in vertex attack, as we aim to detach previously detected communities, once a node from a bridging edge is deleted, there is no need to detach its counterpart unless it also participates in other inter-communities connections. Besides, at each step of the procedure, the attack will focus on the remaining largest connected component of the network, in order to speed up the fragmentation. This process loosely resembles the original idea of weak ties proposed by Granovetter Granovetter (1973) for social networks and later developed in the framework of topological communities by De Meo, Ferrara et al. De Meo et al. (2014).

Figure 2: Fraction of edges bridging communities vs modularity for the ten real networks studied in this work. The dash line is the resulting linear fitting: , with a correlation coefficient .
Facebook 0.80 2.06 528 2888 2981 8 0.260 0.012 0.49 0.12
Twitter 0.85 2.81 108 23370 32831 136 0.056 0.075 0.84 0.36
Google Plus 0.68 3.32 1250 23628 39194 33 0.144 0.173 0.65 0.47
US Power Grid 0.81 2.67 10 4941 6594 40 0.040 0.034 0.86 0.77
Euro Road 0.77 2.41 7 1174 1417 47 0.061 0.073 0.79 0.71
Open Flights 0.64 10.67 594 2939 15677 38 0.144 0.144 0.54 0.32
US Airports 0.35 21.87 2454 1574 17215 11 0.311 0.364 0.06 0.22
Yeast Protein 0.78 2.39 15 1846 2203 179 0.068 0.121 0.59 0.55
H Pylori 0.50 3.88 45 724 1403 25 0.097 0.384 0.45 0.30
C Elegans 0.42 8.94 358 453 2025 9 0.232 0.396 0.37 0.20
Table 1: Topological data for several networks consisting of average modularity, mean degree, second momentum, size of the original network, total number of edges, mean number of modules, relative size of the largest community, fraction of edges linking distinct communities, and relative overall efficiency of MBA method as compared to betweennss-based method for edge and node attacks.

Nonetheless, this procedure is slower than traditional methods since fast community extraction takes, depending on the algorithm used, about , resulting in a lower limit of computation of , where is the total number of nodes and stands for the total number of edges.

As a preemptive measure of our proposed method we show in Fig. 2 the relation between the fraction of nodes in the interface of communities, i.e. the fraction of nodes that make the connection between the different modules extracted from the networks, and the value of the modularity for each one of the real networks that we present in the next section. As expected, we observe an approximate linear (negative) correlation between the fraction of edges bridging communities and the modularity , which is precisely the desired feature that makes the method potentially well posed. As we can see, the infrastructure networks are the ones which better adjust to the linear behavior while the social networks are the worst cases.

Iv Results

We now apply the method to real networks with different topological structures. We investigate the behavior of such systems when topological characteristics are measured only once before the attacking procedure —the so-called simultaneous attack. Besides, the graphs were taken as undirected. For the networks studied here, the different methods for communities extraction, i.e. multilevel Blondel et al. (2008), fast greedy Clauset et al. (2004), walktrap Pons and Latapy (2005), infomap Rosvall et al. (2009), and leading eigenvector Newman (2006), have all a community membership coincidence higher than . Thence, in these simulations we have used the method proposed by Blondel et al. Blondel et al. (2008) because it is the quickest in computing time.

Figure 3: Vertex MBA applied to real networks. Figure portrays the size of the biggest connected component relative to the original network’s size, , in function of the fraction of removed nodes, . Network data are explained in Table 1.
Figure 4: Edge MBA applied to real networks. Figure portrays the size of the biggest connected component relative to the original network’s size, , in function of the fraction of removed links, . Network data are explained in Table 1.

We have chosen three distinct groups of networks: infrastructure (US Power Grid, Euro Road, Open Flights and US Airports) kon (2014a); Watts and Strogatz (1998); kon (2014b); Šubelj and Bajec (2011); kon (2014c); Opsahl et al. (2010); kon (2014d); Opsahl (2011), biological (Yeast Protein, C Elegans and H Pylori) Jeong et al. (2001); Rain et al. (2001); kon (2014e); Duch and Arenas (2005) and social (Facebook, Google+ and Twitter) kon (2014f, g, h); McAuley and Leskovec (2012). In the Euro Road network, nodes represent European cities and edges represent roads. Power Grid stands for the electrical power grid of the Western States of the United States of America. An edge represents a power supply line and a node is either a generator, a transformer, or a substation. The Yeast Protein interaction network is the same as in  Jeong et al. (2001). In the metabolic network of the roundworm Caenorhabditis elegans nodes are metabolites (e.g., proteins) and edges are interactions between them. The Helicobacter pylori is the same protein-protein interaction map as in  Rain et al. (2001). In the Facebook user-user friendship network (NIPS) nodes represent users and edges represent friendship. Similarly, in the Google+ network, an edge means that one user (node) has the other user (node) in her/his circles, while in the Twitter network an edge indicates that both users (nodes) follow each other.

Simulations show that vertex MBA always outperforms the traditional betweenness attack. Initially, both methods are similar but, as bridging nodes are deleted, whole communities start to detach from the core of the graph, resulting in large atomization of the network and hence in an abrupt increase of . On the other hand, in edge MBA the situation changes. As we erase solely edges bridging modules, initial attacks result in a plateau in until we effectively detach whole modules. After this critical point is reached, decreases abruptly, relatively large communities are detached extremely fast, and the whole network falls apart. Attacks usually stop before , depending on the particular modular structure of each network, at a point . This happens precisely when all original communities are detached with no targeted node (edge) left in the remaining clusters, so the network stops functioning as a whole —for instance, information would be stacked within the communities and these structures would not be able to communicate each other. From this point on, one may continue to strike the network (or what is left of it, which is the remaining largest connected component, generally speaking, of the same size of the biggest original community) by some classical attack based on centrality measures such as degree or betweenness. Looking at the figures of the attacks, we can say that vertex MBA shows a second-order phase transitions type behavior (Fig. 3), while edge MBA exhibits a typical first-order phase transition behavior (Fig. 4). In either case, with as the order parameter, the critical points shown in Figs. 3 and  4 mark what we call modular percolation, i.e. the point at which the network is modularly disconnected.

Figure 5: Gain in efficiency of vertex MBA, compared to vertex CBA, as a function of the fraction of removed nodes. Network data are explained in Table 1.

In general, attacking nodes is more efficient than edges since the removal of a vertex always results in the deletion of all edges attached to it. However, depending on the real system studied, vertex or edge attack may not make sense. For instance, in the case of Euro Road one may envisage blocking the traffic between two cities, while removing a node would mean to erase an entire village. On the other hand, in biological systems for example, node deletion makes sense, since individual metabolites are susceptible to be removed from the network.

With these results we now plot the efficiency of the MBA as compared to CBA as a function of for each network in Fig. 5. It is easy to see that most networks reach a gain in efficiency of more than 50% for less than 7% of nodes removed – the more oustanding case being the US Power Grid with more than 95% of gain with approximately 3% of nodes removed. Even in the worst cases (Yeast Protein, H pylori and US Airports) we get more than 60% of gain for less than 16% of vertices deleted.

The overall efficiency of MBA as compared to CBA may be measured by how fast MBA reaches the modular critical point relative to CBA. In other words, we can define the relative overall efficiency as:


where is calculated at the critical point () and is defined as where stands for the fraction of nodes removed at the critical in the MBA approach and is evaluated at the same y-axis point but in the CBA curve. As before, the reference or null method of attack is betweenness-based. The results for the present method of attack on the ten networks, in terms of the previous defined , shown in Fig. 7, tells us that its efficiency strongly depends on the modularity. This is an expected phenomenon since networks with high modularity tend to have a density of edges connecting communities much smaller than the density of internal edges.

Figure 6: Facebook sub-graph depicting its trivial modular structure.

However, in the node removal approach, we have a slightly different picture since the attacks also break down the internal structures of the modules, departing from a linear fit. There is also another important effect that should be noticed: some networks have high modularity, but the inner structure of the modules is very weak. In these cases, the MBA approach does not introduce major gains in efficiency, even though it is still more efficient than traditional CBA. The fact is that in these systems the inner community structure is weak so a more simple attack can be as efficient as the one presented here. On the other side, we may have networks with smaller modularity, but for which the impact of the MBA approach is higher than the impact on networks with higher modularity. That is precisely the special case of the Facebook network extract studied here. As we can see from Fig. 6 the community structure of this network is trivial, with most of the bridging nodes corresponding to the ones with higher degree. Besides, the internal structures of modules are extremely weak with all nodes connected only to a few vertices or even to only one central node.

Figure 7: Overall efficiency of MBA, compared to CBA, as a function of modularity for (a), nodes and (b), edges removal.
Figure 8: Pictorial comparison between the effect of betweenness-based attack, degree-based attack, longest path attack Pu and Cui (2015) and module-based attack in the case of US Power Grid system for 1%, 2% and 3% of nodes removed.

Before arriving at the conclusion, we illustrate on the attack procedure with a case where its performance is remarkably better than previous and well accepted attacking prescriptions. That example is the Power grid of Western USA. In this sense, Fig. 8 summarizes the result of our method of attack as compared to betweenness centrality attack, degree centrality attack, and longest pathway attack Pu and Cui (2015), along with the snapshots of the network when 1%, 2% and 3% of nodes are removed by betweenness centrality ranking (CBA) and by the module-based method (MBA). Remarkably, the present method breaks the original network of 4941 nodes to many fragments smaller than 197 nodes (4% of the original size) by removing mere 164 nodes ( 3%) identified by the procedure. By comparison, in any degree or centrality based procedure, deleting the same amount of nodes, removes only 22% of the original network, i.e. more than 3800 nodes continue to be connected after that. Such extreme atomization of the network is represented graphically by the set of figures on the far right of Fig. 8. Besides, it is promptly seen that the community structure of this network is far from trivial.

V Conclusions

We have presented a module-based attack method which consists of erasing only those structures that bridge distinct communities ordered by betweenness centrality. Computational simulations on many real networks show that the MBA method is more efficient in atomizing networks than traditional procedures based only on centrality criteria. Henceforth, one may say that, in general, the most connected or most central nodes are not necessarily the most important for the network survival. Conceptually speaking, nodes (edges) linking distinct communities are structurally more important and crucial for the modular cohesion of the network than nodes (edges) with high degree or centrality indexes. Obviously, networks with high modularity tend to be in general more fragile against module-based attacks, while networks with intra-module weakness can show smaller differences between different attacking methods.

It should be noted that, regarding the aim of the present work, the communities that emerge from the networks by using the module identification algorithms, have in principle no relation with real communities. However, the organization of a network in coarse grain agglomeration may disclose important information about the structural functionality of complex networks.

In discussions of community detection algorithm, the resolution limit is a topic of debate, however in connection with the attack method proposed here, it is not highly relevant. In fact, what is desirable is a compromise solution in terms of the average module size and the network size. Large modules means a network decomposed in few of them, which is good because many nodes are disconnected once a module is detached from the others. The drawback is that the last module could be still a large part of the original network. On the other hand, a decomposition in many small communities has the advantage of ending with a highly fragmented network, but at the expense of being slower than the other scenario. Therefore the optimum is a situation somehow in the middle, and that is the reason the resolution limit of communities is not of high concern here.

As a final remark, we emphasize that eventual applications of module-based attacks to classes of real systems such as terror, crime or disease related networks might lead to groundbreaking procedures to fight these unwanted threats. We acknowledge CNPq and the Brazilian Federal Police for financial support.


  1. S. V. Buldyrev, R. Parshani, G. Paul, H. E. Stanley, and S. Havlin, Nature 464, 1025 (2010).
  2. C. D. Brummitt, R. M. D’Souza, and E. A. Leicht, Proceedings of the National Academy of Sciences 109, E680 (2012), ISSN 1091-6490.
  3. S. Fortunato, Physics Reports 486, 75 (2010).
  4. T. W. Valente, Science 337, 49 (2012).
  5. R. Cohen, K. Erez, D. ben Avraham, and S. Havlin, Physical Review Letters 86, 3682 (2001).
  6. R. Pastor-Satorras and A. Vespignani, Phys. Rev. E 65 (2002).
  7. J. Xu and H. Chen, Communications of the ACM 51, 58 (2008).
  8. S. Iyer, T. Killingback, B. Sundaram, and Z. Wang, PloS one 8, e59613 (2013).
  9. Us power grid network dataset – KONECT (2014a), URL
  10. T. W. Valente and K. Fujimoto, Social Networks 32, 212 (2010).
  11. W. Hwang, Y.-r. Cho, A. Zhang, and M. Ramanathan, in Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining (2006), pp. 20–23.
  12. M. E. Newman, Physical review E 74, 036104 (2006).
  13. V. D. Blondel, J.-L. Guillaume, R. Lambiotte, and E. Lefebvre, Journal of Statistical Mechanics: Theory and Experiment 2008, P10008 (2008).
  14. M. Barthélemy, Physics Reports 499, 1 (2011).
  15. P. Holme, B. J. Kim, C. N. Yoon, and S. K. Han, Physical Review E 65, 056109 (2002).
  16. M. S. Granovetter, American journal of sociology pp. 1360–1380 (1973).
  17. P. De Meo, E. Ferrara, G. Fiumara, and A. Provetti, Communications of the ACM 57, 78 (2014).
  18. A. Clauset, M. E. Newman, and C. Moore, Physical review E 70, 066111 (2004).
  19. P. Pons and M. Latapy, in Computer and Information Sciences-ISCIS 2005 (Springer, 2005), pp. 284–293.
  20. M. Rosvall, D. Axelsson, and C. T. Bergstrom, The European Physical Journal-Special Topics 178, 13 (2009).
  21. D. J. Watts and S. H. Strogatz, Nature 393, 440 (1998).
  22. Euroroad network dataset – KONECT (2014b), URL
  23. L. Šubelj and M. Bajec, European Physical Journal B 81, 353 (2011).
  24. Openflights network dataset – KONECT (2014c), URL
  25. T. Opsahl, F. Agneessens, and J. Skvoretz, Social Networks 3, 245 (2010).
  26. Us airports network dataset – KONECT (2014d), URL
  27. T. Opsahl, Why Anchorage is not (that) important: Binary ties and Sample selection (2011), URL
  28. H. Jeong, S. P. Mason, A.-L. Barabási, and Z. N. Oltvai, Nature 411, 41 (2001).
  29. J.-C. Rain, L. Selig, H. De Reuse, V. Battaglia, C. Reverdy, S. Simon, G. Lenzen, F. Petel, J. Wojcik, V. Schächter, et al., Nature 409, 211 (2001).
  30. Caenorhabditis elegans network dataset – KONECT (2014e), URL
  31. J. Duch and A. Arenas, Phys. Rev. E 72, 027104 (2005).
  32. Facebook (nips) network dataset – KONECT (2014f), URL
  33. Google+ network dataset – KONECT (2014g), URL
  34. Twitter lists network dataset – KONECT (2014h), URL
  35. J. McAuley and J. Leskovec, in Advances in Neural Information Processing Systems (2012), pp. 548–556.
  36. C.-L. Pu and W. Cui, Physica A: Statistical Mechanics and its Applications 419, 622 (2015).
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