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The misinformation prevention (MP) problem, which aims to find anchor nodes (P-seeds) to start an anti-spread of positive information, has attracted abundant research attention due to its practical application in reducing the risk of social unrest. Existing MP researches ideally assume that the M-seeds (nodes start misinformation spread) are known in advance. However, the misinformation spread usually occurs abruptly so that M-seeds are not easy to be identified as prior knowledge. In this paper, we study the MP problem with uncertain M-seeds. We assume that only a set of suspicious nodes (S-nodes) is known. Each S-node has a transition probability (T-prob) for becoming an M-seed. With different T-prob availability, we propose and study two new research problems: (1) the probabilistic MP (PMP) problem given exactly all the T-probs, and (2) the robust MP (RMP) problem given a confidence interval for each T-prob. For the PMP problem, we propose the prefixed Hybrid sampling based Misinformation Prevention (pHMP) algorithm that return a (1 − 1 / e − ɛ) -approximate solution. For the RMP problem, we optimize the worst-case ratio between the current solution and the optimal one. Two bi-criteria algorithms are proposed that achieve both δ absolute error and (1 − 1 / e − ɛ) 2 (1 − η) and (1 − 1 / e − ɛ) (1 − η) relative error respectively. We conduct extensive experiments on several real world datasets. The results show the proposed algorithms are superior over the baseline algorithm on all datasets, and robust against the changing of different application conditions. [ABSTRACT FROM AUTHOR]

The influence maximization problem asks to find a small number of early adopters of a product in a social network, such that the expected number of total adoptions is maximized over the network. The problem has been well-studied, but most of the studies focus on the case of a single product or purely competitive products. This paper proposes a new influence diffusion model for multiple complementary products, namely, the comparative linear threshold (Com-LT) model. Under the Com-LT model, we model the complementary relation by reducing the thresholds of nodes. With this model, we study two problems: SelfInfMax and CompInfMax. We prove that these two problems are both NP-Hard under the Com-LT model. For both the SelfInfMax and the CompInfMax problem, we theoretically analyze the monotonicity and submodularity, and accordingly leverage lower bound optimization to devise non-trivial effective approximation algorithms. We conduct experiments over 4 real-world datasets. The experimental results demonstrate the correctness and efficiency of the proposed algorithms. • We study the complementary version of the influence maximization problem. • We propose Com-LT model to describe the influence spread of non- competitive products. • We studied the SelfInfMax and the CompInfMax problem and show their hardness. • We propose approximation algorithms with high quality feasible solutions. • We conduct experiments on real networks and show the effectiveness of our algorithms. [ABSTRACT FROM AUTHOR]

In social advertising, the social platform host may run marketing campaigns for multiple competing clients simultaneously. In this case, each client comes up with a budget and an influence spread requirement. The host runs campaigns by allocating a set of seed nodes for each client. If the influence spread triggered by a seed set meets the requirement, the host can earn the budget from the corresponding client. In this paper, we study the problem of Profit Maximization, considering that different seeds incur different costs. Given all the clients' requirements met, we aim to find the optimal seed allocation with minimum cost. Under the competitive K-LT propagation model, we show the Profit Maximization problem is NP-hard and NP-hard to approximate with any factor. To find a feasible solution, we propose an effective algorithm that iteratively selects a candidate set and obtains an approximate allocation. The experimental results over a real-world dataset validate the effectiveness of the proposed methods. [ABSTRACT FROM AUTHOR]

Although the research on nanozymes has attracted widespread attention in recent years, the development of highly active and multifunctional nanozymes remains a challenge. Here, a bifunctional AMP-Cu nanozyme with laccase- and catecholase-like activities was successfully prepared at room temperature with Cu2+ as the metal ion and adenosine-5′-monophosphate (AMP) as the ligand molecule. Based on the excellent catalytic performance of AMP-Cu, a three-channel colorimetric sensor array was constructed using reaction kinetics as the sensing unit to achieve high-throughput detection and identification of six common phenolic compounds at low concentrations. This strategy simplifies the construction of sensor array and demonstrates the capacity to obtain multidimensional data from a single material. Finally, with the assistance of smartphones and homemade dark boxes, a portable on-site detection method for phenolic compounds was developed. This work would contribute to the development of portable sensors and the highly efficient identification of phenolic compounds in complex samples. [Display omitted] • A bifunctional AMP-Cu nanozyme with high laccase- and catecholase-like activity was successfully prepared at room temperature. • A colorimetric sensor array was established based on reaction kinetics. • A portable on-site detection method for phenolic compounds was developed. [ABSTRACT FROM AUTHOR]

Abstract Existing studies on influence maximization (IM) mainly focus on activating a set of influential users (seed nodes). Originated from the seed nodes' promotion actions (e.g., posting an advertising tweet) on social networks, a large influence spread might be triggered. However, in practice it is usually very expensive to have influential users posting original tweets in a promotional event. In contrast, it will incur much lower costs to have influential users reposting tweets and have ordinary users posting original tweets. Inspired by these observations, in this paper, we consider the Holistic Budgeted Influence Maximization (HBIM) problem, which maximizes the influence spread by deploying the budget to select seed nodes (for posting) and boost nodes (for reposting). To tackle the NP-hardness and non-submodularity of the problem, we devise two efficient algorithms with the data-dependent approximation ratios. Extensive experiments on real social networks demonstrate the efficiency and effectiveness of our proposed algorithms. [ABSTRACT FROM AUTHOR]

Abstract Existing works on influence maximization (IM) aim at finding influential online users as seed nodes. Originated from these seed nodes, large online influence spread can be triggered. However, such user-driven perspective limits the IM problem within the purely online environment. Due to the increasing interactions between the cyber world and the physical world, offline events in real world are showing more impact on online information spread. Most IM methods are totally unaware of the cyber–physical interactions and thus their effectiveness is limited when offline events are taken into account. To address this issue, in this paper we consider influence maximization from an online–offline interactive setting and propose the location-driven influence maximization (LDIM) problem. The LDIM problem aims to find the optimal offline deployment of locations and durations to hold events, so as to maximize the online influence spread. We propose a location-driven propagation (LDP) model to describe the online influence spread process triggered by offline events. Under the LDP model, we prove the LDIM problem is NP-hard and computing the objective function is #P-hard. Thus we develop a greedy algorithm over integer lattice and prove its 1 − 1 ∕ e approximation. To overcome the expensive time complexity, we further develop two algorithms with time complexity reduced successively while ensuring provable approximation ratios of 1 − 1 ∕ e − ε and 1 − 1 ∕ e − ε − ε ′ respectively. Experimental results on real datasets show the effectiveness of the proposed algorithms and the significant improvement on scalability. [ABSTRACT FROM AUTHOR]

Influence maximization (IM) in social media aims at finding influential seed users to trigger large online cascading influence spread. Existing studies mainly focus on maximizing the number of the activated nodes, but the diversity of the activated nodes has been mostly ignored even though it is crucial in many real-world applications, e.g., diversity of the influenced crowd can reduce the risk of marketing campaigns, or diversity of recommended items can improve the recommendation quality. In this paper, we study the diversified influence maximization (DIM) problem which aims to select k nodes such that both the number of activated nodes and the diversity of the activated nodes can be maximized. By designing a practical diversity function to model the distribution of activated nodes among all communities, we model the DIM problem by maximizing a parameterized linear combination of the diversity and the active node number. To tackle the NP-hardness of the DIM problem, we propose an efficient diversified influence maximization algorithm, diversified influence maximization via martingale (DIMM), which returns a 1 − 1 ∕ e − ε -approximate solution with at least 1 − 2 ∕ n ℓ probability. Moreover, as the influence spread is highly stochastic and the DIM setting leaves no reserved measures for handling unforeseen events, we present an improved version of the algorithm named adaptive diversified influence maximization via martingale (ADIMM) with the adaptive setting. With the adaptive RI- π α policy, ADIMM is able to return an α (1 − e − 1 ∕ α) -approximate solution with at least 1 − 1 ∕ n ℓ probability. Finally, we experimentally evaluate our algorithm against existing algorithms on large-scale real-world datasets. The experimental results validate the effectiveness and efficiency of our proposed algorithms. [ABSTRACT FROM AUTHOR]

Although nanozymes sensor arrays have the potential to recognize multiple target substances simultaneously, they currently rarely identify phenolic acids in food due to limited catalytic performance and complex preparation conditions of nanozymes. Here, inspired by the structure of polyphenol oxidase, we have successfully prepared a novel gallic acid-Cu (GA-Cu) nanozyme with laccase-like activity. Due to the different catalytic efficiency of GA-Cu nanozymes towards six common phenolic acids, a three-channel colorimetric sensor array was constructed using reaction kinetics as the sensing unit to achieve high-throughput detection and identification of six phenolic acids within a concentration range from 1 to 100 μM. This method avoids the creation of numerous sensing units. Notably, the successful discrimination of six phenolic acids in samples of juice, beer, and wine has been achieved by the sensor array. Finally, aided by smartphones, a portable technique has been devised for the detection of phenolic acids. • A novel GA-Cu nanozyme with laccase-like activity was successfully prepared. • A colorimetric sensor array was established based on reaction kinetics. • phenolic acids were detected and discriminated in juice, beer, and wine. • A portable on-site detection method for phenolic acids was developed. [ABSTRACT FROM AUTHOR]

Abstract Attention is the behavioral and cognitive process of selectively concentrating on small fraction of information while ignoring other perceivable information. Thus, user's attention will influence his decision on the consumption and consequently the performance of recommender systems. When presented a long list of recommendations, users will focus more on the items that draw their attention and skip others even if they suit their tastes. However, existing works in social recommendation mainly generate recommendations based on users' preference and overlook the importance of users' attention. To overcome this limitation, in this paper we propose a probabilistic model HTPF that considers both users' attention and preference in social recommendation. Besides, by analyzing four real-world social recommendation datasets, we observe that social relations have more explicit impact on users' attention than on their item rating values. That is, user's attention on items are easily affected by his social relations while his rating values seem somehow indifferent to social influence. It will be more effective to use social network information to infer users' attention rather than users' preference. Thus, different from existing methods that model the social influence on user's rating values, HTPF focuses on exploiting social influence on user's attention. Further, a scalable variational inference algorithm has been developed for our model to predict how the specific items will draw users' attention and how the items will suit users' tastes. Then HTPF generates the recommendation results based on the weighted combination of these two aspects. Extensive experiments conducted on large real-world datasets validate the superiority of our method over state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The gradient-based meta learning and its approximation algorithms have been widely used in the few-shot scenarios. In practice, it is common for the trained meta-model to employ uniform settings for gradient descent steps across different tasks. However, the meta-model may be biased toward some tasks. The convergence issue occurs that some tasks may see convergence in a few steps while others fail to approach the optimum in the whole inner loop. The bias problem may cause the trained meta-model works well in some tasks but has unexpected bad performance in other tasks, which hurts the generality of the meta-model. To address this issue, in this paper, we formally establish the approximation between the metric-based strategy and gradient descent in meta-test. By directly calculating similarity to classify data, the trained meta-model avoids the convergence issue. We point out that the metric-based methods can closely approximate the gradient descent in meta-test if the representation capability of the derived features and the convergence of the inner loop during meta-training are guaranteed. Based on such observation, we propose a new meta-learning model GMT2 (Gradient-based Meta-Train with Metric-based meta-Test) by combining gradient descent in meta-training with metric-based methods in meta-test. GMT2 employs a new first-order approximation scheme using the adversarial update strategy which not only enhances the feature representation of inner layers, but also allows enough inner gradient steps without calculating second-order derivatives. Experiments show that GMT2 achieves better efficiency and competitive accuracy comparing with popular meta-learning models. [ABSTRACT FROM AUTHOR]

• We propose a unified community-based seed triggering model. • Two new variants of influence maximization and prevention problems are defined. • Efficient algorithms with provable approximation guarantees are designed. • Extensive experiments demonstrate the effectiveness of our methods. Given a social network, the classical influence maximization (IM) and misinformation prevention (MP) problems both adopt similar seed triggering models, i.e., convincing k specific users to become seed nodes by material incentive (e.g. free products). However, in real life, those chosen seeds may not be willing to spread information as expected, which will affect the final diffusion. Instead of convincing one single user, we can target a user community, in the hope that some of them may turn into propagation seeds voluntarily. This community-based seed (Com-seed) triggering model can be used in real-world applications such as distributing flyers or offering discounts in local communities, where the objective is to maximize the promotion effect with the given budget constraints. In this paper, we aim to maximize the influence or minimize the misinformation spread by finding an optimal community-based budget allocation under the Com-seed triggering models. We present new formulations of the influence maximization and misinformation prevention problems from the community perspective and design effective and scalable algorithms to solve these new problems. With intricately designed community-based sampling schemes and approximation guarantee of the greedy approach over integer lattice, our algorithms can achieve (1 - 1 / e - ∊) -approximation results. Experiments show our methods outperform all baselines and run faster than the state-of-the-art methods in both influence maximization and misinformation prevention problems, which demonstrate the effectiveness and scalability of the proposed algorithms. [ABSTRACT FROM AUTHOR]