A Consistency Rule for Graph Isomorphism Problem

International audienceThis paper describes an algorithm for graph isomorphism problem. A consistency rule is proposed to detect as soon as possible the isomorphism permutation. The algorithm, called CRGI, tries to find an isomorphism between two in- put graphs through a backtracking exploration that uses a proposed consistency rule to prune the tree-search. This rule is based on changing cases positions of one adjacency matrix to obtain exactly the second adjacency matrix, according to a permutation that must be defined. If such permutation exists, an isomorphism is detected. The proposed rule is able to prune as early as possible unfruitful branches of the tree-search which leads to reduce the practical time com- plexity. Experimental results comparing CRGI with other popular algorithms show the effectiveness of CRGI especially for random graphs and trees

A Consistency Rule for Graph Isomorphism Problem

International audienceThis paper describes an algorithm for graph isomorphism problem. A consistency rule is proposed to detect as soon as possible the isomorphism permutation. The algorithm, called CRGI, tries to find an isomorphism between two in- put graphs through a backtracking exploration that uses a proposed consistency rule to prune the tree-search. This rule is based on changing cases positions of one adjacency matrix to obtain exactly the second adjacency matrix, according to a permutation that must be defined. If such permutation exists, an isomorphism is detected. The proposed rule is able to prune as early as possible unfruitful branches of the tree-search which leads to reduce the practical time com- plexity. Experimental results comparing CRGI with other popular algorithms show the effectiveness of CRGI especially for random graphs and trees

Tracking COVID-19 by Tracking Infectious Trajectories

Nowadays, the coronavirus pandemic has and is still causing large numbers of deaths and infected people. Although governments all over the world have taken severe measurements to slow down the virus spreading (e.g., travel restrictions, suspending all sportive, social, and economic activities, quarantines, social distancing, etc.), a lot of persons have died and a lot more are still in danger. Indeed, a recently conducted study [1] has reported that 79% of the confirmed infections in China were caused by undocumented patients who had no symptoms. In the same context, in numerous other countries, since coronavirus takes several days before the emergence of symptoms, it has also been reported that the known number of infections is not representative of the real number of infected people (the actual number is expected to be much higher). That is to say, asymptomatic patients are the main factor behind the large quick spreading of coronavirus and are also the major reason that caused governments to lose control over this critical situation. To contribute to remedying this global pandemic, in this article, we propose an IoTa investigation system that was specifically designed to spot both undocumented patients and infectious places. The goal is to help the authorities to disinfect high-contamination sites and confine persons even if they have no apparent symptoms. The proposed system also allows determining all persons who had close contact with infected or suspected patients. Consequently, rapid isolation of suspicious cases and more efficient control over any pandemic propagation can be achieved

New Techniques for Limiting Misinformation Propagation

This paper focuses on limiting misinformation propagation in networks. Its first contribution is introducing the notion of vaccinated observers, which is a node enriched with additional power. Vaccination is adding, locally, a plugin or asking for the help of a trusted third party, called a trusted authority. The plugin or the authority is able to detect if the received information is misinformation or not. Vaccinated Observers must stop forwarding detected misinformation. Based on this notion, two algorithms for limiting misinformation are proposed. The second contribution of the paper is an algorithm based on Moving Observers for locating a strong adversary diffusion source. This algorithm selects a random subset of nodes as observers for a random period $\Delta$ . This means that the observer subset may change over time in a randomized manner. Consequently, the strong adversary diffusion source can’t have global knowledge about observers positions. Having these positions by the diffusion source will make its localization by the observers more complicated, even impossible. The third contribution is proposing an algorithm for stopping misinformation propagation based on a punishment strategy. This algorithm has a very simple principle design and it assumes that an authority or a mechanism $A$ is available. The authority $A$ has the ability to detect if the received information is misinformation or not. If a node $n_{i}$ receives information $m$ from its neighbor $n_{j}$ and $m$ is detected, by $n_{i}$ via the authority $A$ , as misinformation then $n_{j}$ is punished for a period $pp$ ( $pp$ stands for punishment period). If the node $n_{j}$ repeats this action for $n$ time then the punishment period increases to $n*pp$ . The punishment in this algorithm is stopping the forwarding of the information received from a punished node $n_{j}$ . The simulation results show that the proposed techniques are both efficient and accurate while locating the diffusion source. Consequently, misinformation propagation is limited