Vaccination allocation in large dynamic networks

Event propagation over a network is a complex and frequently studied human phenomena [1–3]. Historical examples of information exchange between humans in a social network, past and present, can be seen during colonial expansion of the British Empire [4], memes passed between friends on any assortment of social networks on the internet [1] and transmission of human or animal pathogens such as the virus H1N1 over airways [5]. In the information age computer networks can mirror these types of information exchange, with event information being passed along from node to node when network neighbors communicate through various network protocols [6].

This information can be useful, whether in the form of postal delivery of early colonial directives and parcels [4], neutral, shared irrelevant Facebook posts [7], or detrimental, such as human pathogens or computer attacks [5, 8]. In this paper we will use consistent terminology of a negative event for the purpose of visualization for the reader. Some examples may include: how to spread, as in infection, or how to prevent the spread, as in vaccination, of this negative event, or attack. Malicious attacks can take many forms, such as, malware, viruses, undesirable information, or even memes that might violate a networks posting policy [8, 9]. In terms of this paper “vaccination”? is defined as a set of nodes that can be inoculated, removed, and/or protected from these malicious attacks. The main assumption is that if these specific sets of nodes are given pre-emptive measures then the attack will only be effective on a minimum number of nodes in the network.

The development and growth of networks has led to a diverse number of academic domains with shared properties and/or unique properties. Network traversal by means of graph theory [10], network communication protocols [6], static networks [11], dynamic networks [9] and network security [12] are network domains with similar terminology and visualizations. If a similarity exists across such diversity then it is possible to explore a similar solution that models important aspects borrowed from similar domains. This paper examines networks with nodes that are sparsely connected that may have a hierarchical structure that can be exploited using parts of graph theory to develop a solution.

Large networks can suffer disastrous results if an attack is allowed to migrate over a network. Various problems have been considered in the literature regarding this issue. Stopping a known attack at a source node before it can infect its neighbor nodes is a trivial problem. Once the attack spreads beyond the source node, however, its complexity rises as more nodes are exposed and infected through available edges [13]. Another trivial situation for an active infection network is if the amount of vaccine available is much larger than the number of non-infected adjacent nodes to any infected nodes. The more typical case occurs when non-infected nodes outnumber the amount of vaccine by a large degree [11]. This limitation causes a cost-benefit solution for which choices must be made in order to create an outcome that is favorable to the network administrators. Giving system administrators a variety of implementations allows them to choose the best option, or possible combinations of near optimal solutions, for their system [14].

Several solutions exist to prevent the spread of attacks over closed static systems; however these solutions have limited application to dynamic systems. An effective solution for arbitrary static networks with sparse connectivity is called Data-Aware Vaccine Allocation [11] or DAVA. DAVA is a framework that treats infected sub-graphs as super nodes and all its infected node neighbors as rooted bidirectional sub-trees that has a representational optimal solution in the form of a set of adjacent neighbors of the infected root super node. The framework determines this set by using a benefit system derived from a probability of propagation set, an infection set, and the main network graph. Thoroughly discussed by Zhang and Prakash the DAVA algorithm has been proven effective for “vaccine” distribution over an arbitrary network given prior information [11]. However this solution has limited application if new nodes are being added to the network over a consistent time interval.

Large networks that are dynamic have vulnerabilities because of the changes made to the network in real time [9]. Complications arise when new nodes vary in their importance to the networks due to location of edges and varying probability to infection [8]. For example a node could be added to the network that has edges to nodes that were not connected before or that create a cycle that was not present before the edge was added. This addition may or may not change the overall benefit of the sub-tree that contains the node due to probability changes or dominance changes caused by this addition. In a static system this addition or removal now would represent a new graph which requires the entire network to be examined from the start. This paper seeks to make the distinction that these new nodes will not necessarily alter the current benefit solution of its associated sub-trees thus would not alter the optimal solution; it would require less work than recalculating the entire tree with new benefits and optimal solution. As a bonus, overall network information would be gained that could be applied to machine learning to enhance the information about this network. This gain might be exploited when examining local or global solutions.

Similarly to a multi-task learning problem [2], dynamic network node vaccination, or removal, has a main task, namely the optimal solution and then sub tasks to calculate and determine the overall benefit for top-k vaccination order subgroups. Individual nodes that have outlier tendencies might alter any current branch benefit, thus altering the top-k benefit order. It is from this concept that the idea of local solutions are of greater interest than global solutions for dynamic networks. If these local changes could be determined to have little or no changes to the benefits of the global sub-tree to which the local solution belongs, then it maybe possible to exploit this information to make an algorithm more efficient.

This paper presents a dynamic network algorithm, VAILDN, that is able to receive nodes sequentially from a network and then devise an optimal set of vaccination nodes to ensure maximum protection of the network. While performing effectively on static solutions DAVA group of algorithms slow down due to the fact that the global solution always must be determined, even though changes may occur only for local solutions. This paper will show that it is possible to adjust the optimal solution without examining the entire network which will save time over static algorithms. In addition this paper will show that VAILDN has the same level of accuracy as do DAVA type algorithms.

Dynamic solutions using dominator trees also present challenges that are not considered when using static solutions. These challenges include the addition or removal of edges to the graph that may redraw the dominator tree if a cycle is created or destroyed. In a dynamic environment it is possible that children may be detected before the parents in terms of the overall graph. This will not affect the global solution however, as edges are added, local connections and dominance are built that could be exploited for time saving when any parent does arrive.

In this paper first related works are discussed in “Preliminaries” section. The main algorithm is broken into three phases and presented in “Experiments” section. “Results” section describes the experiments in which this algorithm was compared to random baseline algorithms and vaccination methods for static networks. The final sections discuss the experimental results and the conclusions, including suggestions for future work.

This paper combines several resources into a cohesive solution that would be able to deal with active infections in a large dynamic network. Static solutions for networks having prior information were examined [15] and interesting solutions are present that could be expanded to dynamic networks by using a multi-task approach. Topics for consideration include, epidemiology, multi-task learning, data-aware vaccination allocation, dominance trees, network hierarchy, and large dynamic network optimization.

The topic of population vaccination has been well studied with a variety of models based on different approaches. Sometimes the global solution may not be obvious [16] or maybe difficult to obtain [17]. A localized approach could target specific groups that are known to have higher propagation probabilities or more frequent contact with infected. A global static approach could be too costly due to number of nodes in the network [14]. A dynamic solution that examines edges could be beneficial in calculating the optimal solution [16].

In references to “Data-Aware Vaccination Allocation” [11] this paper demonstrates that the Data Aware Vaccination problem is NP-hard using a reduction from the MinKU set problem [18]. In order to demonstrate the variety of the problem and the general application of DAVA two common discrete-time models were used for virus infection, the susceptible-infected-removed (SIR) model and the immune response (IR) model. The SIR model [19] used for the general case, is based on chicken-pox-like infections, during which a time cycle consists of two phases, an independent infection probability and a recovery probability \(\delta \). If an infection is transferred from an infected node to a healthy node successfully (the probability of the infection is given by the weights of the edges) then that healthy node will be infected during next round. If recovery of an infected node occurs, then that node is “cured” and no longer will able to participate in the epidemic. The second model, the IR model [11], is a specific case of the SIR model during which the interaction between an infected and a healthy node happens only once, and the recovery probability, \(\delta \) = 1. The solution algorithms suggested by Zhang and Prakash the DAVA, DAVA-prune, and DAVA fast algorithms were demonstrated to perform better within static networks in which an infection already was in progress when compared with five different static network algorithms.

This paper presents the propagation probabilities based on population simulations. Due to the related natures of networks and human communities [20] it was worth exploring to simulate how human communities interact in order to assign non-random meaning to the propagation. A normalization of a population simulation yields sufficient data values to test the research. People can be considered nodes and their contact can be considered edges. Much like human populations contact among nodes is dynamic and should benefit from a dynamic solution. Contacts of certain individuals may have more weight than others thus the importance of calculating a local solution needs to be stressed. For example the elderly or infirm might be of concern only to their local group associations rather than to the network as a whole [17].

Network exploration and node importance are two fundamental concepts to this work. Research in the field of network exploration has resulted in many good algorithms and two popular methods include depth-first-search (dfs) [21] and breadth-first-search (bfs) [10]. Dfs can be used to maximize propagation results as well as to define subdominators and dominators [11] as a dominator tree is built. Dfs is an effective tool for network exploration when the entire network is known and is relatively sparse. Highly connected graphs will benefit more from bfs due to the exploration of individual levels before the depth of each sub-tree is explored. This distinction is important for this study because the assumption for a dominance tree is based around the fact that the nodes are sparsely connected. Dominator trees are used to discover the interconnectedness of nodes in a sparse network [21]. A dominator tree will yield sub-trees that show the relative connection between nodes, thus can yield maximum solutions based on those sub-trees [11]. If the structure of a dominator tree is correct [22] a new relational mapping of a network can be built quickly to determine which parent nodes singularly expose their children to infection. A dominator tree shows that if there is a non-unique cycle between two nodes x and y then x and y will be the only common nodes to all paths. As nodes in a network become more and more connected, dominance will lose its significance as many nodes begin to share the same dominator. This is why the assumption was made in this study that the graphs had some hierarchical structure present and that they were sparsely connected.

The building of the dominator tree becomes more challenging in a dynamic setting. Shortest path algorithms were shown to be useful to determine dominance in linear time [23]. In a dynamic setting it is not simply the shortest path alone which determines dominance. Any edge that creates a cycle between nodes in a sub-tree will upset the dominance and create the need to recalculate dominance for any nodes who are siblings to any node in the cycle. Another consideration for dominance is when an edge is removed the original dominance must be restored to all nodes that were either in the shortest path to the dominator or those neighbors of the shortest path nodes.

A multi-task solution is necessary for large dynamic networks due to the number of sub-problems created by sequential node entry into the network. Collaborative Online Multi-task Learning (COML) [2], an important aspect considered in this study was written to deal with the discovery of individual distributions that may not be properly recognized by the main task. It is the dynamic nature of the sub-trees that are formed with sequential node entry into the network that can form these subtasks. COML demonstrates that subtasks can be learned effectively at the same time as the main task. Thus information about individuals and the group are maintained independent of each other. Predictions made from using these learned distributions could be useful in making classifications for unknown instances that enter the system.

One of the main libraries used for implementation was the networkX library. This package is a library of graph objects and a variety of associated functions beneficial to this study. It was possible to use the graph representations as a cornerstone for the building, analysis, and traversal of the network. This graph could then be dissected into its representative dominance tree in order to yield the final solution [24].

These experiments demonstrate how a real time solution for a dynamic network can outperform a static solution. VAILDN is able to compute the solution as edges are examined in real time. This suggests that a local solution to the vaccination problem may be a better overall solution as the network grows in size or has a global solution that is difficult to calculate. Random vaccination is fast but it is not as effective as VAILDN.

The main assumption for algorithms that use dominance as a basis is that either there are sparse connections or there is some overall hierarchical structure to the network. Network analysis in a static situation can reveal this type of information with many of the basic tools already established, such as depth first or breadth first searches. Dynamic networks, however present a complex field of parameters that can confound a single method of finding a solution. A dynamic network that is relatively stable might mimic a static environment; however edges can still be presented that could disrupt the assumptions of dominance or sparsity.

In VAILDN adding edges allowed the exploitation of the current dominance tree in order to establish new dominance of siblings and children. The removal of edges presented a specific challenge because after removing an edge the current tree structure gave little information about how to restore dominance. This is in contrast to adding edges where the dominance tree structure is already present. The method that was used relied on determining the siblings of each level for all the grandchildren of the common dominator. Removing edges was the most time consuming part of the algorithm, if it could be improved the efficiency will increase even more.

In Fig. 5 VAILDN still compares similarly to the DAVA algorithms with regards to accuracy. The change in the weight calculation does little to affect the benefit solution. These results suggest that VAILDN is still beneficial with varying weight differences.

A real time solution is able to deal with the reality of a modern and changing network dynamic. A solution can be determined quickly, as only the information local to the incoming, or removed, edge is considered. The accuracy of VAILDN is comparable to the static solution with diminishing losses. This means a network can make real time decisions about how the information is being distributed over the network. Dynamic networks offer difficult challenges with regard to issue detection. This algorithm is able to determine a real time solution with the assumption that the network is sparse.

Densely connected graphs create a low level dominance tree that makes finding the optimal solution challenging. To compensate for this a few future directions are open for exploration. This solution can be further refined by adding a factor that will calculate how much effect an edge propagation might have on its sub-tree solution. This will allow no change to the solution set if a small benefit is added, thus saving time. This could represent highly unlikely, yet present, connections between network members.

To continue this research, community clusters, rather than dominance trees, might be another structure for detecting the optimal solution. Even if a community is densely connected, the communities themselves maybe sparsely connected. This implies that with community detection, a local outbreak solution maybe able to be determined quickly offering a better solution than the global solution. A machine learning approach could be applied to determine which solution, dominance tree or community clustering, presents the most beneficial to the host network in that configuration.

This paper used terminology that indicated the information that was propagated was negative in nature (Table 2). This may not always be the case. The development of algorithms for information distribution with limited resources may be able to build on this algorithm as a real time solution for information distribution. The real time solution presented here is a heuristic solution that demonstrates the value of information propagation over a dynamic network.