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- Optimization (5)
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- Decision making (2)
- Maximum principle (2)
- Message passing (2)
- Multi agent systems (2)
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- Bike Sharing Systems (1)
- Bird nest detection (1)
- Budget constraint (1)
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Articles 1 - 30 of 46
Full-Text Articles in Physical Sciences and Mathematics
Managing Egress Of Crowd During Infrastructure Disruption, Teck Hou Teng, Shih-Fen Cheng, Trong-Nghia Truong, Hoong Chuin Lau
Managing Egress Of Crowd During Infrastructure Disruption, Teck Hou Teng, Shih-Fen Cheng, Trong-Nghia Truong, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
In a large indoor environment such as a sports arena or convention center, smooth egress of crowd after an event can be seriously affected if infrastructure such as elevators and escalators break down. In this paper, we propose a novel crowd simulator known as SIM-DISRUPT for simulating egress scenarios in non-emergency situations. To surface the impact of disrupted infrastructure on the egress of crowd, SIM-DISRUPT includes features that allow users to specify selective disruptions as well as strategies for controlling the distribution and egress choices of crowd. Using SIM-DISRUPT, we investigate effects of crowd distribution, egress choices and infrastructure disruptions …
Orienteering Problem: A Survey Of Recent Variants, Solution Approaches And Applications, Aldy Gunawan, Hoong Chuin Lau, Pieter Vansteenwegen
Orienteering Problem: A Survey Of Recent Variants, Solution Approaches And Applications, Aldy Gunawan, Hoong Chuin Lau, Pieter Vansteenwegen
Research Collection School Of Computing and Information Systems
Duplicate record, see https://ink.library.smu.edu.sg/sis_research/3271. The Orienteering Problem (OP) has received a lot of attention in the past few decades. The OP is a routing problem in which the goal is to determine a subset of nodes to visit, and in which order, so that the total collected score is maximized and a given time budget is not exceeded. A number of typical variants has been studied, such as the Team OP, the (Team) OP with Time Windows and the Time Dependent OP. Recently, a number of new variants of the OP was introduced, such as the Stochastic OP, the …
Lexicon Knowledge Extraction With Sentiment Polarity Computation, Zhaoxia Wang, Vincent Joo Chuan Tong, Pingcheng Ruan, Fang Li
Lexicon Knowledge Extraction With Sentiment Polarity Computation, Zhaoxia Wang, Vincent Joo Chuan Tong, Pingcheng Ruan, Fang Li
Research Collection School Of Computing and Information Systems
Sentiment analysis is one of the most popular natural language processing techniques. It aims to identify the sentiment polarity (positive, negative, neutral or mixed) within a given text. The proper lexicon knowledge is very important for the lexicon-based sentiment analysis methods since they hinge on using the polarity of the lexical item to determine a text's sentiment polarity. However, it is quite common that some lexical items appear positive in the text of one domain but appear negative in another. In this paper, we propose an innovative knowledge building algorithm to extract sentiment lexicon knowledge through computing their polarity value …
Validating Social Media Data For Automatic Persona Generation, Jisun An, Haewoon Kwak, Bernard J Jansen
Validating Social Media Data For Automatic Persona Generation, Jisun An, Haewoon Kwak, Bernard J Jansen
Research Collection School Of Computing and Information Systems
Using personas during interactive design has considerable potential for product and content development. Unfortunately, personas have typically been a fairly static technique. In this research, we validate an approach for creating personas in real time, based on analysis of actual social media data in an effort to automate the generation of personas. We validate that social media data can be implemented as an approach for automating generating personas in real time using actual YouTube social media data from a global media corporation that produces online digital content. Using the organization's YouTube channel, we collect demographic data, customer interactions, and topical …
Orienteering Problem: A Survey Of Recent Variants, Solution Approaches And Applications, Aldy Gunawan, Hoong Chuin Lau, Pieter Vansteenwegen
Orienteering Problem: A Survey Of Recent Variants, Solution Approaches And Applications, Aldy Gunawan, Hoong Chuin Lau, Pieter Vansteenwegen
Research Collection School Of Computing and Information Systems
The Orienteering Problem (OP) has received a lot of attention in the past few decades. The OP is a routing problem in which the goal is to determine a subset of nodes to visit, and in which order, so that the total collected score is maximized and a given time budget is not exceeded. A number of typical variants has been studied, such as the Team OP, the (Team) OP with Time Windows and the Time Dependent OP. Recently, a number of new variants of the OP was introduced, such as the Stochastic OP, the Generalized OP, the Arc OP, …
An Agent-Based Approach To Human Migration Movement, Larry Lin, Kathleen M. Carley, Shih-Fen Cheng
An Agent-Based Approach To Human Migration Movement, Larry Lin, Kathleen M. Carley, Shih-Fen Cheng
Research Collection School Of Computing and Information Systems
How are the populations of the world likely to shift? Which countries will be impacted by sea-level rise? This paper uses a country-level agent-based dynamic network model to examine shifts in population given network relations among countries, which influences overall population change. Some of the networks considered include: alliance networks, shared language networks, economic influence networks, and proximity networks. Validation of model is done for migration probabilities between countries, as well as for country populations and distributions. The proposed framework provides a way to explore the interaction between climate change and policy factors at a global scale.
Zero++: Harnessing The Power Of Zero Appearances To Detect Anomalies In Large-Scale Data Sets, Guansong Pang, Kai Ming Ting, David Albrecht, Huidong Jin
Zero++: Harnessing The Power Of Zero Appearances To Detect Anomalies In Large-Scale Data Sets, Guansong Pang, Kai Ming Ting, David Albrecht, Huidong Jin
Research Collection School Of Computing and Information Systems
This paper introduces a new unsupervised anomaly detector called ZERO++ which employs the number of zero appearances in subspaces to detect anomalies in categorical data. It is unique in that it works in regions of subspaces that are not occupied by data; whereas existing methods work in regions occupied by data. ZERO++ examines only a small number of low dimensional subspaces to successfully identify anomalies. Unlike existing frequencybased algorithms, ZERO++ does not involve subspace pattern searching. We show that ZERO++ is better than or comparable with the state-of-the-art anomaly detection methods over a wide range of real-world categorical and numeric …
A Decomposition Method For Estimating Recursive Logit Based Route Choice Models, Tien Mai, Fabian Bastin, Emma Frejinger
A Decomposition Method For Estimating Recursive Logit Based Route Choice Models, Tien Mai, Fabian Bastin, Emma Frejinger
Research Collection School Of Computing and Information Systems
Fosgerau et al. (2013) recently proposed the recursive logit (RL) model for route choice problems, that can be consistently estimated and easily used for prediction without any sampling of choice sets. Its estimation however requires solving many large-scale systems of linear equations, which can be computationally costly for real data sets. We design a decomposition (DeC) method in order to reduce the number of linear systems to be solved, opening the possibility to estimate more complex RL based models, for instance mixed RL models. We test the performance of the DeC method by estimating the RL model on two networks …
Reducing Adaptation Latency For Multi-Concept Visual Perception In Outdoor Environments, Maggie Wigness, John G. Rogers, Luis Ernesto Navarro-Serment, Arne Suppe, Bruce A. Draper
Reducing Adaptation Latency For Multi-Concept Visual Perception In Outdoor Environments, Maggie Wigness, John G. Rogers, Luis Ernesto Navarro-Serment, Arne Suppe, Bruce A. Draper
Research Collection School Of Computing and Information Systems
Multi-concept visual classification is emerging as a common environment perception technique, with applications in autonomous mobile robot navigation. Supervised visual classifiers are typically trained with large sets of images, hand annotated by humans with region boundary outlines followed by label assignment. This annotation is time consuming, and unfortunately, a change in environment requires new or additional labeling to adapt visual perception. The time is takes for a human to label new data is what we call adaptation latency. High adaptation latency is not simply undesirable but may be infeasible for scenarios with limited labeling time and resources. In this paper, …
Achieving Economic And Environmental Sustainabilities In Urban Consolidation Center With Bicriteria Auction, Stephanus Daniel Handoko, Hoong Chuin Lau, Shih-Fen Cheng
Achieving Economic And Environmental Sustainabilities In Urban Consolidation Center With Bicriteria Auction, Stephanus Daniel Handoko, Hoong Chuin Lau, Shih-Fen Cheng
Research Collection School Of Computing and Information Systems
Consolidation lies at the heart of the last-mile logistics problem. Urban consolidation centers (UCCs) have been set up to facilitate such consolidation all over the world. To the best of our knowledge, most-if not all-of the UCCs operate on volume-based fixed-rate charges. To achieve environmental sustainability while ensuring economic sustainability in urban logistics, we propose, in this paper, a bicriteria auction mechanism for the automated assignment of last-mile delivery orders to transport resources. We formulate and solve the winner determination problem of the auction as a biobjective programming model. We then present a systematic way to generate the Pareto frontier …
Tasker: Behavioral Insights Via Campus-Based Experimental Mobile Crowd-Sourcing, Thivya Kandappu, Nikita Jaiman, Randy Tandriansyah Daratan, Archan Misra, Shih-Fen Cheng, Cen Chen, Hoong Chuin Lau, Deepthi Chander, Koustuv Dasgupta
Tasker: Behavioral Insights Via Campus-Based Experimental Mobile Crowd-Sourcing, Thivya Kandappu, Nikita Jaiman, Randy Tandriansyah Daratan, Archan Misra, Shih-Fen Cheng, Cen Chen, Hoong Chuin Lau, Deepthi Chander, Koustuv Dasgupta
Research Collection School Of Computing and Information Systems
While mobile crowd-sourcing has become a game-changer for many urban operations, such as last mile logistics and municipal monitoring, we believe that the design of such crowdsourcing strategies must better accommodate the real-world behavioral preferences and characteristics of users. To provide a real-world testbed to study the impact of novel mobile crowd-sourcing strategies, we have designed, developed and experimented with a real-world mobile crowd-tasking platform on the SMU campus, called TA$Ker. We enhanced the TA$Ker platform to support several new features (e.g., task bundling, differential pricing and cheating analytics) and experimentally investigated these features via a two-month deployment of TA$Ker, …
Human-Centred Design For Silver Assistants, Zhiwei Zheng, Di Wang, Ailiya Borjigin, Chunyan Miao, Ah-Hwee Tan, Cyril Leung
Human-Centred Design For Silver Assistants, Zhiwei Zheng, Di Wang, Ailiya Borjigin, Chunyan Miao, Ah-Hwee Tan, Cyril Leung
Research Collection School Of Computing and Information Systems
To alleviate the rapidly increasing need of the healthcare workforce to serve the enormous ageing population, leveraging intelligent and autonomous caring agents is one promising way. Working towards the design and development of dedicated personal silver assistants for older adults, we follow the human-centred design approach. Specifically, we identify a number of human factors that affect the user experience of the older adults and develop an agent named Mobile Intelligent Silver Assistant (MISA) by applying these human factors. Integrating multiple reusable services onto one platform, MISA acts as a single point of contact while simultaneously providing easy and convenient access …
An Intelligent System For Personalized Conference Event Recommendation And Scheduling, Aldy Gunawan, Hoong Chuin Lau, Pradeep Varakantham, Wenjie Wang
An Intelligent System For Personalized Conference Event Recommendation And Scheduling, Aldy Gunawan, Hoong Chuin Lau, Pradeep Varakantham, Wenjie Wang
Research Collection School Of Computing and Information Systems
Many conference mobile apps today lack the intelligent feature to automatically generates optimal schedules based on delegates' preferences. This entails two major challenges: (a) identifying preferences of users; and (b) given the preferences, generating a schedule that optimizes his preferences. In this paper, we specifically focus on academic conferences, where users are prompted to input their preferred keywords. Our key contribution is an integrated conference scheduling agent that automatically recognizes user preferences based on keywords, provides a list of recommended talks and optimizes user schedule based on these preferences. To demonstrate the utility of our integrated conference scheduling agent, we …
A Reinforcement Learning Framework For Trajectory Prediction Under Uncertainty And Budget Constraint, Truc Viet Le, Siyuan Liu, Hoong Chuin Lau
A Reinforcement Learning Framework For Trajectory Prediction Under Uncertainty And Budget Constraint, Truc Viet Le, Siyuan Liu, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
We consider the problem of trajectory prediction, where a trajectory is an ordered sequence of location visits and corresponding timestamps. The problem arises when an agent makes sequential decisions to visit a set of spatial locations of interest. Each location bears a stochastic utility and the agent has a limited budget to spend. Given the agent's observed partial trajectory, our goal is to predict the agent's remaining trajectory. We propose a solution framework to the problem that incorporates both the stochastic utility of each location and the budget constraint. We first cluster the agents into groups of homogeneous behaviors called …
Enhancing Local Search With Adaptive Operator Ordering And Its Application To The Time Dependent Orienteering Problem, Aldy Gunawan, Hoong Chuin Lau, Kun Lu
Enhancing Local Search With Adaptive Operator Ordering And Its Application To The Time Dependent Orienteering Problem, Aldy Gunawan, Hoong Chuin Lau, Kun Lu
Research Collection School Of Computing and Information Systems
No abstract provided.
A Fast Algorithm For Personalized Travel Planning Recommendation, Aldy Gunawan, Hoong Chuin Lau, Kun Lu
A Fast Algorithm For Personalized Travel Planning Recommendation, Aldy Gunawan, Hoong Chuin Lau, Kun Lu
Research Collection School Of Computing and Information Systems
With the pervasive use of recommender systems and web/mobile applications such as TripAdvisor and Booking.com, an emerging interest is to generate personalized tourist routes based on a tourist’s preferences and time budget constraints, often in real-time. The problem is generally known as the Tourist Trip Design Problem (TTDP) which is a route-planning problem on multiple Points of Interest (POIs). TTDP can be considered as an extension of the classical problem of Team Orienteering Problem with Time Windows (TOPTW). The objective of the TOPTW is to determine a fixed number of routes that maximize the total collected score. The TOPTW also …
New Developments In Metaheuristics And Their Applications: Selected Extended Contributions From The 10th Metaheuristics International Conference (Mic 2013), Hoong Chuin Lau, Günther R. Raidl, Pascal Van Hentenryck
New Developments In Metaheuristics And Their Applications: Selected Extended Contributions From The 10th Metaheuristics International Conference (Mic 2013), Hoong Chuin Lau, Günther R. Raidl, Pascal Van Hentenryck
Research Collection School Of Computing and Information Systems
No abstract provided.
Robust Repositioning To Counter Unpredictable Demand In Bike Sharing Systems, Supriyo Ghosh, Michael Trick, Pradeep Varakantham
Robust Repositioning To Counter Unpredictable Demand In Bike Sharing Systems, Supriyo Ghosh, Michael Trick, Pradeep Varakantham
Research Collection School Of Computing and Information Systems
Bike Sharing Systems (BSSs) experience a significant loss in customer demand due to starvation (empty base stations precluding bike pickup) or congestion (full base stations precluding bike return). Therefore, BSSs operators reposition bikes between stations with the help of carrier vehicles. Due to unpredictable and dynamically changing nature of the demand, myopic reasoning typically provides a below par performance. We propose an online and robust repositioning approach to minimise the loss in customer demand while considering the possible uncertainty in future demand. Specifically, we develop a scenario generation approach based on an iterative two player game to compute a strategy …
Sequential Decision Making For Improving Efficiency In Urban Environments, Pradeep Varakantham
Sequential Decision Making For Improving Efficiency In Urban Environments, Pradeep Varakantham
Research Collection School Of Computing and Information Systems
Rapid "urbanization" (more than 50% of world's population now resides in cities) coupled with the natural lack of coordination in usage of common resources (ex: bikes, ambulances, taxis, traffic personnel, attractions) has a detrimental effect on a wide variety of response (ex: waiting times, response time for emergency needs) and coverage metrics (ex: predictability of traffic/security patrols) in cities of today. Motivated by the need to improve response and coverage metrics in urban environments, my research group is focussed on building intelligent agent systems that make sequential decisions to continuously match available supply of resources to an uncertain demand for …
Scalable Greedy Algorithms For Task/Resource Constrained Multi-Agent Stochastic Planning, Pritee Agrawal, Pradeep Varakantham, William Yeoh
Scalable Greedy Algorithms For Task/Resource Constrained Multi-Agent Stochastic Planning, Pritee Agrawal, Pradeep Varakantham, William Yeoh
Research Collection School Of Computing and Information Systems
Synergistic interactions between task/resource allocation and stochastic planning exist in many environments such as transportation and logistics, UAV task assignment and disaster rescue. Existing research in exploiting these synergistic interactions between the two problems have either only considered domains where tasks/resources are completely independent of each other or have focussed on approaches with limited scalability. In this paper, we address these two limitations by introducing a generic model for task/resource constrained multi-agent stochastic planning, referred to as TasC-MDPs. We provide two scalable greedy algorithms, one of which provides posterior quality guarantees. Finally, we illustrate the high scalability and solution performance …
Serendipity-Driven Celebrity Video Hyperlinking, Shujun Yang, Lei Pang, Chong-Wah Ngo, Benoit Huet
Serendipity-Driven Celebrity Video Hyperlinking, Shujun Yang, Lei Pang, Chong-Wah Ngo, Benoit Huet
Research Collection School Of Computing and Information Systems
This demo showcases the utility of video hyperlinks with celebrities as the link anchors and their social circles as targets, aiming to help users quickly explore the aboutness of a celebrity by link traversal. Through content analysis, our system embeds hyperlinks into videos such that users can click-and-jump between celebrity faces in different videos to get-to-know their social circles. One peculiar feature is the ability of the system in providing links that maximize users' chance encounter, or serendipitous experience, beyond information need. Our system is enabled by two key components, name-face association and diversity-based ranking, for the aboutness and serendipity …
Exemplar-Driven Top-Down Saliency Detection Via Deep Association, Shengfeng He, Rynson W. H. Lau, Qingxiong Yang
Exemplar-Driven Top-Down Saliency Detection Via Deep Association, Shengfeng He, Rynson W. H. Lau, Qingxiong Yang
Research Collection School Of Computing and Information Systems
Top-down saliency detection is a knowledge-driven search task. While some previous methods aim to learn this "knowledge" from category-specific data, others transfer existing annotations in a large dataset through appearance matching. In contrast, we propose in this paper a locateby-exemplar strategy. This approach is challenging, as we only use a few exemplars (up to 4) and the appearances among the query object and the exemplars can be very different. To address it, we design a two-stage deep model to learn the intra-class association between the exemplars and query objects. The first stage is for learning object-to-object association, and the second …
Designing And Comparing Multiple Portfolios Of Parameter Configurations For Online Algorithm Selection, Aldy Gunawan, Hoong Chuin Lau, Mustafa Misir
Designing And Comparing Multiple Portfolios Of Parameter Configurations For Online Algorithm Selection, Aldy Gunawan, Hoong Chuin Lau, Mustafa Misir
Research Collection School Of Computing and Information Systems
Algorithm portfolios seek to determine an effective set of algorithms that can be used within an algorithm selection framework to solve problems. A limited number of these portfolio studies focus on generating different versions of a target algorithm using different parameter configurations. In this paper, we employ a Design of Experiments (DOE) approach to determine a promising range of values for each parameter of an algorithm. These ranges are further processed to determine a portfolio of parameter configurations, which would be used within two online Algorithm Selection approaches for solving different instances of a given combinatorial optimization problem effectively. We …
Dual Formulations For Optimizing Dec-Pomdp Controllers, Akshat Kumar, Hala Mostafa, Shlomo Zilberstein
Dual Formulations For Optimizing Dec-Pomdp Controllers, Akshat Kumar, Hala Mostafa, Shlomo Zilberstein
Research Collection School Of Computing and Information Systems
Decentralized POMDP is an expressive model for multi-agent planning. Finite-state controllers (FSCs)---often used to represent policies for infinite-horizon problems---offer a compact, simple-to-execute policy representation. We exploit novel connections between optimizing decentralized FSCs and the dual linear program for MDPs. Consequently, we describe a dual mixed integer linear program (MIP) for optimizing deterministic FSCs. We exploit the Dec-POMDP structure to devise a compact MIP and formulate constraints that result in policies executable in partially-observable decentralized settings. We show analytically that the dual formulation can also be exploited within the expectation maximization (EM) framework to optimize stochastic FSCs. The resulting EM algorithm …
Self-Organizing Neural Network For Adaptive Operator Selection In Evolutionary Search, Teck Hou Teng, Stephanus Daniel Handoko, Hoong Chuin Lau
Self-Organizing Neural Network For Adaptive Operator Selection In Evolutionary Search, Teck Hou Teng, Stephanus Daniel Handoko, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
Evolutionary Algorithm is a well-known meta-heuristics paradigm capable of providing high-quality solutions to computationally hard problems. As with the other meta-heuristics, its performance is often attributed to appropriate design choices such as the choice of crossover operators and some other parameters. In this chapter, we propose a continuous state Markov Decision Process model to select crossover operators based on the states during evolutionary search. We propose to find the operator selection policy efficiently using a self-organizing neural network, which is trained offline using randomly selected training samples. The trained neural network is then verified on test instances not used for …
Strategic Planning For Setting Up Base Stations In Emergency Medical Systems, Supriyo Ghosh, Pradeep Varakantham
Strategic Planning For Setting Up Base Stations In Emergency Medical Systems, Supriyo Ghosh, Pradeep Varakantham
Research Collection School Of Computing and Information Systems
Emergency Medical Systems (EMSs) are an important component of public health-care services. Improving infrastructure for EMS and specifically the construction of base stations at the ”right” locations to reduce response times is the main focus of this paper. This is a computationally challenging task because of the: (a) exponentially large action space arising from having to consider combinations of potential base locations, which themselves can be significant; and (b) direct impact on the performance of the ambulance allocation problem, where we decide allocation of ambulances to bases. We present an incremental greedy approach to discover the placement of bases that …
Modeling Autobiographical Memory In Human-Like Autonomous Agents, Di Wang, Ah-Hwee Tan, Chunyan Miao
Modeling Autobiographical Memory In Human-Like Autonomous Agents, Di Wang, Ah-Hwee Tan, Chunyan Miao
Research Collection School Of Computing and Information Systems
Although autobiographical memory is an important part of the human mind, there has been little effort on modeling autobiographical memory in autonomous agents. With the motivation of developing human-like intelligence, in this paper, we delineate our approach to enable an agent to maintain memories of its own and to wander in mind. Our model, named Autobiographical Memory-Adaptive Resonance Theory network (AM-ART), is designed to capture autobiographical memories, comprising pictorial snapshots of one’s life experiences together with the associated context, namely time, location, people, activity, and emotion. In terms of both network structure and dynamics, AM-ART coincides with the autobiographical memory …
Efficient 3d Dental Identification Via Signed Feature Histogram And Learning Keypoint Detection, Zhiyuan Zhang, Sim Heng Ong, Xin Zhong, Kelvin W. C. Foong
Efficient 3d Dental Identification Via Signed Feature Histogram And Learning Keypoint Detection, Zhiyuan Zhang, Sim Heng Ong, Xin Zhong, Kelvin W. C. Foong
Research Collection School Of Computing and Information Systems
Current methods of dental identification are mainly based on 2D dental radiographs which suffer from speed and accuracy limitations. In this paper, we present an efficient dental identification approach based on 3D dental models. We propose a novel shape descriptor, the Signed Feature Histogram (SFH), which is highly discriminative and can be easily computed to describe the local surface. Based on the SFH, a learning keypoint detection method is adopted to accurately detect the desired keypoints on both antemortem (AM) and postmortem (PM) models. For a given PM model, the optimal initial alignment to the AM model to be matched …
Simultaneous Optimization And Sampling Of Agent Trajectories Over A Network, Hala Mostafa, Akshat Kumar, Hoong Chuin Lau
Simultaneous Optimization And Sampling Of Agent Trajectories Over A Network, Hala Mostafa, Akshat Kumar, Hoong Chuin Lau
Research Collection School Of Computing and Information Systems
We study the problem of optimizing the trajectories of agents moving over a network given their preferences over which nodes to visit subject to operational constraints on the network. In our running example, a theme park manager optimizes which attractions to include in a day-pass to maximize the pass’s appeal to visitors while keeping operational costs within budget. The first challenge in this combinatorial optimization problem is that it involves quantities (expected visit frequencies of each attraction) that cannot be expressed analytically, for which we use the Sample Average Approximation. The second challenge is that while sampling is typically done …
Robust Influence Maximization, Meghna Lowalekar, Pradeep Varakantham, Akshat Kumar
Robust Influence Maximization, Meghna Lowalekar, Pradeep Varakantham, Akshat Kumar
Research Collection School Of Computing and Information Systems
Influence Maximization is the problem of finding a fixed size set of nodes, which will maximize the expected number of influenced nodes in a social network. The number of influenced nodes is dependent on the influence strength of edges that can be very noisy. The noise in the influence strengths can be modeled using a random noise or adversarial noise model. It has been shown that all random processes that independently affect edges of the graph can be absorbed into the activation probabilities themselves and hence random noise can be captured within the independent cascade model. On the other hand, …