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1. Measuring the Impact of Influence on Individuals: Roadmap to Quantifying Attitude

   Fu, Xiaoyun; Padmanabhan, Madhavan; Kumar, Raj; Basu, Samik; Dorius, Shawn; Pavan, A. (October 2020, {IEEE/ACM} International Conference on Advances in Social Networks Analysis and Mining, {ASONAM})

   Atzmuller, Martin; Coscia, Michele; Missaoui, Rokia (Ed.)

   Influence diffusion has been central to the study of the propagation of information in social networks, where influence is typically modeled as a binary property of entities: influenced or not influenced. We introduce the notion of attitude, which, as described in social psychology, is the degree by which an entity is influenced by the information. We present an information diffusion model that quantifies the degree of influence, i.e., attitude of individuals, in a social network. With this model, we formulate and study the attitude maximization problem. We prove that the function for computing attitude is monotonic and sub-modular, and the attitude maximization problem is NP-Hard. We present a greedy algorithm for maximization with an approximation guarantee of $(1-1/e)$. Using the same model, we also introduce the notion of ``actionable'' attitude with the aim to study the scenarios where attaining individuals with high attitude is objectively more important than maximizing the attitude of the entire network. We show that the function for computing actionable attitude, unlike that for computing attitude, is non-submodular but is approximately submodular. We present an approximation algorithm for maximizing actionable attitude in a network. We experimentally evaluated our algorithms and studied empirical properties of the attitude of nodes in the network such as spatial and value distribution of high attitude nodes. 

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2. Decentralized Online Influence Maximization

   https://doi.org/10.1109/Allerton49937.2022.9929315  

   Bayiz, Yigit E.; Topcu, Ufuk (September 2022, 58th Annual Allerton Conference on Communication, Control, and Computing (Allerton))

   We consider the problem of finding the maximally influential node in random networks where each node influences every other node with constant yet unknown probability. We develop an online algorithm that learns the relative influences of the nodes. It relaxes the assumption in the existing literature that a central observer can monitor the influence spread globally. The proposed algorithm delegates the online updates to the nodes on the network; hence requires only local observations at the nodes. We show that using an explore-then-commit learning strategy, the cumulative regret accumulated by the algorithm over horizon T approaches O(T2/3) for a network with a large number of nodes. Additionally, we show that, for fixed T, the worst case-regret grows linearly with the number n of nodes in the graph. Numerical experiments illustrate this linear dependence for Chung-Lu models. The experiments also demonstrate that ε-greedy learning strategies can achieve similar performance to the explore-then-commit strategy on Chung-Lu models. 

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3. Semi-boustrophedon coverage with a dubins vehicle

   https://doi.org/10.1109/IROS.2017.8206451  

   Lewis, Jeremy S.; Edwards, William; Benson, Kelly; Rekleitis, Ioannis; O'Kane, Jason M. (September 2017, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS))

   This paper addresses the problem of generating coverage paths-that is, paths that pass within some sensor footprint of every point in an environment-for vehicles with Dubins motion constraints. We extend previous work that solves this coverage problem as a traveling salesman problem (TSP) by introducing a practical heuristic algorithm to reduce runtime while maintaining near-optimal path length. Furthermore, we show that generating an optimal coverage path is NP-hard by reducing from the Exact Cover problem, which provides justification for our algorithm's conversion of Dubins coverage instances to TSP instances. Extensive experiments demonstrate that the algorithm does indeed produce length paths comparable to optimal in significantly less time. 

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4. Measuring the impact of influence on individuals: roadmap to quantifying attitude

   Fu, Xiaoyun; Padmanabhan, Madhavan; Kumar, Raj Gaurav; Basu, Samik; Dorius, Shawn; Pavan, A. (July 2021, Social network analysis and mining)

   null (Ed.)

   Diffusion of information in social network has been the focus of intense research in the recent past decades due to its significant impact in shaping public discourse through group/individual influence. Existing research primarily models influence as a binary property of entities: influenced or not influenced. While this is a useful abstraction, it discards the notion of degree of influence, i.e., certain individuals may be influenced ``more'' than others. We introduce the notion of \emph{attitude}, which, as described in social psychology, is the degree by which an entity is influenced by the information. Intuitively, attitude captures the number of distinct neighbors of an entity influencing the latter. We present an information diffusion model (AIC model) that quantifies the degree of influence, i.e., attitude of individuals, in a social network. With this model, we formulate and study attitude maximization problem. We prove that the function for computing attitude is monotonic and sub-modular, and the attitude maximization problem is NP-Hard. We present a greedy algorithm for maximization with an approximation guarantee of $(1-1/e)$. In the context of AIC model, we study two problems, with the aim to investigate the scenarios where attaining individuals with high attitude is objectively more important than maximizing the attitude of the entire network. In the first problem, we introduce the notion of \emph{actionable attitude}; intuitively, individuals with actionable attitude are likely to ``act'' on their attained attitude. We show that the function for computing actionable attitude, unlike that for computing attitude, is non-submodular and however is \emph{approximately submodular}. We present approximation algorithm for maximizing actionable attitude in a network. In the second problem, we consider identifying the number of individuals in the network with attitude above a certain value, a threshold. In this context, the function for computing the number of individuals with attitude above a given threshold induced by a seed set is \emph{neither submodular nor supermodular}. We present heuristics for realizing the solution to the problem. We experimentally evaluated our algorithms and studied empirical properties of the attitude of nodes in network such as spatial and value distribution of high attitude nodes. 

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5. An Efficient Solving Approach for the p‐Dispersion Problem Based on the Distance‐Based Spatially Informed Property

   https://doi.org/10.1111/gean.12392  

   Oh, Changwha; Kim, Hyun; Chun, Yongwan (February 2024, Geographical Analysis)

   Thep‐dispersion problem is a spatial optimization problem that aims to maximize the minimum separation distance among all assigned nodes. This problem is characterized by an innate spatial structure based on distance attributes. This research proposes a novel approach, named thedistance‐based spatially informed property(D‐SIP) method to reduce the problem size of thep‐dispersion instances, facilitating a more efficient solution while maintaining optimality in nearly all cases. The D‐SIP is derived from investigating the underlying spatial characteristics from the behaviors of thep‐dispersion problem in determining the optimal location of nodes. To define the D‐SIP, this research applies Ripley'sK‐function to the different types of point patterns, given that the optimal solutions of thep‐dispersion problem are strongly associated with the spatial proximity among points discovered by Ripley'sK‐function. The results demonstrate that the D‐SIP identifies collective dominances of optimal solutions, leading to buildingthe spatially informed p‐dispersion model. The simulation‐based experiments show that the proposed method significantly diminishes the size of problems, improves computational performance, and secures optimal solutions for 99.9% of instances (999 out of 1,000 instances) under diverse conditions. 

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