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Drug repositioning is an economically efficient strategy used to discover new indications for existing drugs beyond their original approvals, expanding their applicability and usage to address challenges in disease treatment. In recent years, deep-learning techniques for drug repositioning have gained much attention. While most deep learning-based research methods focus on encoding drugs and diseases by extracting feature information from neighbors in the network, they often pay little attention to the potential relationships between the features of drugs and diseases, leading to imprecise encoding of drugs and diseases. To address this, we design a dual-feature drug repositioning neural network (DFDRNN) model to achieve precise encoding of drugs and diseases. DFDRNN uses two features to represent drugs and diseases: the similarity feature and the association feature. The model incorporates a self-attention mechanism to design two dual-feature extraction modules for achieving precisely encoding of drugs and diseases: the intra-domain dual-feature extraction (IntraDDFE) module and the inter-domain dual-feature extraction (InterDDFE) module. The IntraDDFE module extracts features from a single domain (drug or disease domain), while the InterDDFE module extracts features from the mixed domain (drug and disease domain). In particular, the feature is changed by InterDDFE, ensuring a precise encoding of drugs and diseases. Finally, a cross-dual-domain decoder is designed to predict drug-disease associations in both the drug and disease domains. Compared to six state-of-the-art methods, DFDRNN outperforms others on four benchmark datasets, with an average AUROC of 0.946 and an average AUPR of 0.597.

The Critical Node Problem (CNP) is concerned with identifying the critical nodes in a complex network. These nodes play a significant role in maintaining the connectivity of the network, and removing them can negatively impact network performance. CNP has been studied extensively due to its numerous real-world applications. Among the different versions of CNP, CNP-1a has gained the most popularity. The primary objective of CNP-1a is to minimize the pair-wise connectivity in the remaining network after deleting a limited number of nodes from a network. Due to the NP-hard nature of CNP-1a, many heuristic/metaheuristic algorithms have been proposed to solve this problem. However, most existing algorithms start with a random initialization, leading to a high cost of obtaining an optimal solution. To improve the efficiency of solving CNP-1a, a knowledge-guided genetic algorithm named K2GA has been proposed. Unlike the standard genetic algorithm framework, K2GA has two main components: a pretrained neural network to obtain prior knowledge on possible critical nodes, and a hybrid genetic algorithm with local search for finding an optimal set of critical nodes based on the knowledge given by the trained neural network. The local search process utilizes a cut node-based greedy strategy. The effectiveness of the proposed knowledgeguided genetic algorithm is verified by experiments on 26 realworld instances of complex networks. Experimental results show that K2GA outperforms the state-of-the-art algorithms regarding the best, median, and average objective values, and improves the best upper bounds on the best objective values for eight realworld instances., Comment: 14 pages, 13 figures

Given a graph, the minimum dominating set (MinDS) problem is to identify a smallest set $D$ of vertices such that every vertex not in $D$ is adjacent to at least one vertex in $D$. The MinDS problem is a classic $\mathcal{NP}$-hard problem and has been extensively studied because of its many disparate applications in network analysis. To solve this problem efficiently, many heuristic approaches have been proposed to obtain a good solution within an acceptable time limit. However, existing MinDS heuristic algorithms are always limited by various tie-breaking cases when selecting vertices, which slows down the effectiveness of the algorithms. In this paper, we design an efficient local search algorithm for the MinDS problem, named DmDS -- a dual-mode local search framework that probabilistically chooses between two distinct vertex-swapping schemes. We further address limitations of other algorithms by introducing vertex selection criterion based on the frequency of vertices added to solutions to address tie-breaking cases, and a new strategy to improve the quality of the initial solution via a greedy-based strategy integrated with perturbation. We evaluate DmDS against the state-of-the-art algorithms on seven datasets, consisting of 346 instances (or families) with up to tens of millions of vertices. Experimental results show that DmDS obtains the best performance in accuracy for almost all instances and finds much better solutions than state-of-the-art MinDS algorithms on a broad range of large real-world graphs., Comment: 11 pages, 2 figures, 13 tables

DNA computing, a nontraditional computing mechanism, provides a feasible and effective method for solving NP-hard problems because of the vast parallelism and high-density storage of DNA molecules. Although DNA computing has been exploited to solve various intractable computational problems, such as the Hamiltonian path problem, SAT problem, and graph coloring problem, there has been little discussion of designing universal DNA computing-based models, which can solve a class of problems. In this paper, by leveraging the dynamic and enzyme-free properties of DNA strand displacement, we propose a universal model named DCMSubset for solving subset problems in graph theory. The model aims to find a minimum (or maximum) set satisfying given constraints. For each element x involved in a given problem, DCMSubset uses an exclusive single-stranded DNA molecule to model x as well as a specific DNA complex to model the relationship between x and other elements. Based on the proposed model, we conducted simulation and biochemical experiments on three kinds of subset problems, a minimum dominating set, maximum independent set, and minimum vertex cover. We observed that DCMSubset can also be used to solve the graph coloring problem. Moreover, we extended DCMSubset to a model for solving the SAT problem. The results of experiments showed the feasibility and university of the proposed method. Our results highlighted the potential for DNA strand displacement to act as a computation tool to solve NP-hard problems.

Intelligence agents and multi-agent systems play important roles in scenes like the control system of grouped drones, and multi-agent navigation and obstacle avoidance which is the foundational function of advanced application has great importance. In multi-agent navigation and obstacle avoidance tasks, the decision-making interactions and dynamic changes of agents are difficult for traditional route planning algorithms or reinforcement learning algorithms with the increased complexity of the environment. The classical multi-agent reinforcement learning algorithm, Multi-agent deep deterministic policy gradient(MADDPG), solved precedent algorithms' problems of having unstationary training process and unable to deal with environment randomness. However, MADDPG ignored the temporal message hidden beneath agents' interaction with the environment. Besides, due to its CTDE technique which let each agent's critic network to calculate over all agents' action and the whole environment information, it lacks ability to scale to larger amount of agents. To deal with MADDPG's ignorance of the temporal information of the data, this article proposes a new algorithm called MADDPG-LSTMactor, which combines MADDPG with Long short term memory (LSTM). By using agent's observations of continuous timesteps as the input of its policy network, it allows the LSTM layer to process the hidden temporal message. Experimental result demonstrated that this algorithm had better performance in scenarios where the amount of agents is small. Besides, to solve MADDPG's drawback of not being efficient in scenarios where agents are too many, this article puts forward a light-weight MADDPG (MADDPG-L) algorithm, which simplifies the input of critic network. The result of experiments showed that this algorithm had better performance than MADDPG when the amount of agents was large.

The maximum independent set (MIS) problem, a classical NP-hard problem with extensive applications in various areas, aims to find the largest set of vertices with no edge among them. Due to its computational intractability, it is difficult to solve the MIS problem effectively, especially on large graphs. Employing heuristic approaches to obtain a good solution within an acceptable amount of time has attracted much attention in literature. In this paper, we propose an efficient local search framework for MIS called ARIR, which encompasses two main parts: a lightweight adaptive mechanism and a novel inexact efficient reduction rule to simplify instances. Based on ARIR, three algorithms -- ARIR-I, ARIR-II, and ARIR-III -- are developed by adopting three distinct reduction strategies. We conduct experiments on five benchmarks, encompassing 92 instances. Compared with six state-of-the-art algorithms, our ARIR-based algorithms offer the best accuracy on the majority of instances, while obtaining competitive results on the remaining instances., Comment: 11 pages, 0 figures

For the purpose of maximizing the spread of influence caused by a certain small number k of nodes in a social network, we are asked to find a k-subset of nodes (i.e., a seed set) with the best capacity to influence the nodes not in it. This problem of influence maximization (IM) has wide application, belongs to subset problems, and is NP-hard. To solve it, we should theoretically examine all seed sets and evaluate their influence spreads, which is time-consuming. Therefore, metaheuristic strategies are generally employed to gain a good seed set within a reasonable time. We observe that many algorithms for the IM problem only adopt a uniform mechanism in the whole solution search process, which lacks a response measure when the algorithm becomes trapped in a local optimum. To address this issue, we propose a phased hybrid evaluation-enhanced (PHEE) approach for IM, which utilizes two distinct search strategies to enhance the search of optimal solutions: a randomized range division evolutionary (RandRDE) algorithm to improve the solution quality, and a fast convergence strategy. Our approach is evaluated on 10 real-world social networks of different sizes and types. Experimental results demonstrate that our algorithm is efficient and obtains the best influence spread for all the datasets compared with three state-of-the-art algorithms, outperforms the time consuming CELF algorithm on four datasets, and performs worse than CELF on only two networks.

The metric (resp. edge metric) dimension of a simple connected graph $G$, denoted by dim$(G)$ (resp. edim$(G)$), is the cardinality of a smallest vertex subset $S\subseteq V(G)$ for which every two distinct vertices (resp. edges) in $G$ have distinct distances to a vertex of $S$. It is an interesting topic to discuss the relation between dim$(G)$ and edim$(G)$ for some class of graphs $G$. In this paper, we settle two open problems on this topic for a widely studied class of graphs, called unicyclic graphs. Specifically, we introduce four classes of subgraphs to characterize the structure of a unicyclic graph whose metric (resp . edge metric) dimension is equal to the lower bound on this invariant for unicyclic graphs. Based on this, we determine the exact values of dim$(G)$ and edim$(G)$ for all unicyclic graphs $G$.

The $k$-rainbow independent domination number of a graph $G$, denoted $\gamma_{\rm rik}(G)$, is the cardinality of a smallest set consisting of two vertex-disjoint independent sets $V_1$ and $V_2$ for which every vertex in $V(G)\setminus (V_1\cup V_2)$ has neighbors in both $V_1$ and $V_2$. This domination invariant was proposed by {\v{S}}umenjak, Rall and Tepeh in (Applied Mathematics and Computation 333(15), 2018: 353-361), which allows to reduce the problem of computing the independent domination number of the generalized prism $G {\Box} K_k$ to an integer labeling problem on $G$. They proved a Nordhaus-Gaddum-type theorem: $5\leq \gamma_{\rm rik}(G)+\gamma_{\rm rik}(\overline{G})\leq n+3$ for every graph $G$ of order $n\geq 3$, where $\overline{G}$ is the complement of $G$. In this paper, we improve this result by showing that if $G$ is not isomorphic to the 5-cycle, then $5\leq \gamma_{\rm rik}(G)+\gamma_{\rm rik}(\overline{G})\leq n+2$. Moreover, we show that the problem of deciding whether a graph has a $k$-rainbow independent dominating function of a given weight is $\mathcal{NP}$-complete. Our results respond some open questions proposed by \v{S}umenjak, et al., Comment: 9 pages, 1 figure

A total $k$-coloring of a graph is an assignment of $k$ colors to its vertices and edges such that no two adjacent or incident elements receive the same color. The Total Coloring Conjecture (TCC) states that every simple graph $G$ has a total ($\Delta(G)+2$)-coloring, where $\Delta(G)$ is the maximum degree of $G$. This conjecture has been confirmed for planar graphs with maximum degree at least 7 or at most 5, i.e., the only open case of TCC is that of maximum degree 6. It is known that every planar graph $G$ of $\Delta(G) \geq 9$ or $\Delta(G) \in \{7, 8\}$ with some restrictions has a total $(\Delta(G) + 1)$-coloring. In particular, in [Shen and Wang, "On the 7 total colorability of planar graphs with maximum degree 6 and without 4-cycles", Graphs and Combinatorics, 25: 401-407, 2009], the authors proved that every planar graph with maximum degree 6 and without 4-cycles has a total 7-coloring. In this paper, we improve this result by showing that every diamond-free and house-free planar graph of maximum degree 6 is totally 7-colorable if every 6-vertex is not incident with two adjacent 4-cycles or not incident with three cycles of size $p,q,\ell$ for some $\{p,q,\ell\}\in \{\{3,4,4\},\{3,3,4\}\}$.