Vital node identification is crucial for understanding the topology of network structures as well as controlling the spreading process in complex systems. Even though many node ranking metrics have been designed for vital node identification in static networks, there is a lack of research in temporal systems. And how the temporal information influence node ranking is still unknown. In this work, we propose a temporal information gathering (TIG) process for temporal networks. TIG-process, as a node's importance metric, can be used to do the node ranking. As a framework, TIG-process can be applied to explore the impact of temporal information on nodes' significance. The key point of the TIG-process is that node's importance relies on the importance of its neighborhood. There are four parameters, including the temporal information gathering depth n, temporal distance matrix D, initial information c and weighting function f. We observe that TIG-process can degenerate to classic metrics both from static and temporal networks by proper combination of these four parameters. In addition, fastest arrival distance based TIG-process (fad-tig) performs much better in quantifying nodes' efficiency and nodes' spreading influence than the one based on temporal shortest distance. For the fad-tig process, we can find an optimal gathering depth n which makes the TIG-process perform the best when n is
Based on advancements in deep sequencing technology and microbiology, increasing evidence indicates that microbes inhabiting humans modulate various host physiological phenomena, thus participating in various disease pathogeneses. Owing to increasing availability of biological data, further studies on the establishment of efficient computational models for predicting potential associations are required. In particular, computational approaches can also reduce the discovery cycle of novel microbe-disease associations and further facilitate disease treatment, drug design, and other scientific activities. This study aimed to develop a model based on the random walk on hypergraph for microbe-disease association prediction (RWHMDA). As a class of higher-order data representation, hypergraph could effectively recover information loss occurring in the normal graph methodology, thus exclusively illustrating multiple pair-wise associations. Integrating known microbe-disease associations in the Human Microbe-Disease Association Database (HMDAD) and the Gaussian interaction profile kernel similarity for microbes, random walk was then implemented for the constructed hypergraph. Consequently, RWHMDA performed optimally in predicting the underlying disease-associated microbes. More specifically, our model displayed AUC values of 0.8898 and 0.8524 in global and local leave-one-out cross-validation (LOOCV), respectively. Furthermore, three human diseases (asthma, Crohn’s disease, and type 2 diabetes) were studied to further illustrate prediction performance. Moreover, 8, 10, and 8 of the 10 highest ranked microbes were confirmed through recent experimental or clinical studies. In conclusion, RWHMDA is expected to display promising potential to predict disease-microbe associations for follow-up experimental studies and facilitate the prevention, diagnosis, treatment, and prognosis of complex human diseases.
A signed network represents how a set of nodes are connected by two logically contradictory types of links: positive and negative links. In a signed products network, two products can be complementary (purchased together) or substitutable (purchased instead of each other). Such contradictory types of links may play dramatically different roles in the spreading process of information, opinion, behaviour etc. In this work, we propose a self-avoiding pruning (SAP) random walk on a signed network to model e.g. a user's purchase activity on a signed products network. A SAP walk starts at a random node. At each step, the walker moves to a positive neighbour that is randomly selected, the previously visited node is removed and each of its negative neighbours are removed independently with a pruning probability r. We explored both analytically and numerically how signed network topological features influence the key performance of a SAP walk: the evolution of the pruned network resulted from the node removals, the length of a SAP walk and the visiting probability of each node. These findings in signed network models are further verified in two real-world signed networks. Our findings may inspire the design of recommender systems regarding how recommendations and competitions may influence consumers' purchases and products' popularity.products where two products are connected if when a user is purchasing one product the other product is recommended 3 by the online retail platform like Amazon [24][25][26]. However, these models have not considered the substitute relations between products and the fact that once a product has been purchased, its substitutable products will be unlikely to be purchased afterwards. Hence, we propose in this work a self-avoiding pruning (SAP) walk on a signed network to model, e.g. a user's purchase behaviour on a signed network of products. As shown in figure 1, a SAP walk starts at a random node in a signed network at t=0. At each step, the walker moves from its current location node i to a positive neighbour 4 j that is randomly selected, its previous location, i.e. node i is removed from the signed network 5 and each of node iʼs negative neighbours is removed independently with probability r. The walker repeats such steps until there is no new location to move to. Since each node pair can be connected by either a positive or negative link, but not both, the walker could equivalently, at each step, remove each negative neighbour of its current visiting node i independently with probability r, then move to a random positive neighbour j and afterwards remove the previously visited node i.In the context of a signed product network, a SAP walk may model the purchase trajectory of a user on the network of products: initially, the user purchases a random product and afterwards buys a random complementary product of his/her previous purchase; however, the user will not buy the same product repetitively and unlikely buy the substitutable products of what he/she has bought. If the negative layer is...
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