Learning Concept Interestingness for Identifying Key Structures from Social Networks

This is the author accepted manuscript. The final version is available from IEEE via the DOI in this recordIdentifying key structures from social networks that aims to discover hidden patterns and extract valuable information is an essential task in the network analysis realm. These different structure detection tasks can be integrated naturally owing to the topological nature of key structures. However, identifying key network structures in most studies has been performed independently, leading to huge computational overheads. To address this challenge, this paper proposes a novel approach for handling key structures identification tasks simultaneously under the unified Formal Concept Analysis (FCA) framework. Specifically, we first implement the FCA-based representation of a social network and then generate the fine-grained knowledge representation, namely concept. Then, an efficient concept interestingness calculation algorithm suitable for social network scenarios is proposed. Next, we then leverage concept interestingness to quantify the hidden relations between concepts and network structures. Finally, an efficient algorithm for jointly key structures detection is developed based on constructed mapping relations. Extensive experiments conducted on real-world networks demonstrate that the efficiency and effectiveness of our proposed approach.Fundamental Research Funds for the Central Universitie

Enhancing Network Resilience through Machine Learning-powered Graph Combinatorial Optimization: Applications in Cyber Defense and Information Diffusion

With the burgeoning advancements of computing and network communication technologies, network infrastructures and their application environments have become increasingly complex. Due to the increased complexity, networks are more prone to hardware faults and highly susceptible to cyber-attacks. Therefore, for rapidly growing network-centric applications, network resilience is essential to minimize the impact of attacks and to ensure that the network provides an acceptable level of services during attacks, faults or disruptions. In this regard, this thesis focuses on developing effective approaches for enhancing network resilience. Existing approaches for enhancing network resilience emphasize on determining bottleneck nodes and edges in the network and designing proactive responses to safeguard the network against attacks. However, existing solutions generally consider broader application domains and possess limited applicability when applied to specific application areas such as cyber defense and information diffusion, which are highly popular application domains among cyber attackers. These solutions often prioritize general security measures and may not be able to address the complex targeted cyberattacks [147, 149]. Cyber defense and information diffusion application domains usually consist of sensitive networks that attackers target to gain unauthorized access, potentially causing significant financial and reputational loss. This thesis aims to design effective, efficient and scalable techniques for discovering bottleneck nodes and edges in the network to enhance network resilience in cyber defense and information diffusion application domains. We first investigate a cyber defense graph optimization problem, i.e., hardening active directory systems by discovering bottleneck edges in the network. We then study the problem of identifying bottleneck structural hole spanner nodes, which are crucial for information diffusion in the network. We transform both problems into graph-combinatorial optimization problems and design machine learning based approaches for discovering bottleneck points vital for enhancing network resilience. This thesis makes the following four contributions. We first study defending active directories by discovering bottleneck edges in the network and make the following two contributions. (1) To defend active directories by discovering and blocking bottleneck edges in the graphs, we first prove that deriving an optimal defensive policy is #P-hard. We design a kernelization technique that reduces the active directory graph to a much smaller condensed graph. We propose an effective edge-blocking defensive policy by combining neural network-based dynamic program and evolutionary diversity optimization to defend active directory graphs. The key idea is to accurately train the attacking policy to obtain an effective defensive policy. The experimental evaluations on synthetic AD attack graphs demonstrate that our defensive policy generates effective defense. (2) To harden large-scale active directory graphs, we propose reinforcement learning based policy that uses evolutionary diversity optimization to generate edge-blocking defensive plans. The main idea is to train the attacker’s policy on multiple independent defensive plan environments simultaneously so as to obtain effective defensive policy. The experimental results on synthetic AD graphs show that the proposed defensive policy is highly effective, scales better and generates better defensive plans than our previously proposed neural network-based dynamic program and evolutionary diversity optimization approach. We then investigate discovering bottleneck structural hole spanner nodes in the network and make the following two contributions. (3) To discover bottleneck structural hole spanner nodes in large-scale and diverse networks, we propose two graph neural network models, GraphSHS and Meta-GraphSHS. The main idea is to transform the SHS identification problem into a learning problem and use the graph neural network models to learn the bottleneck nodes. Besides, the Meta-GraphSHS model learns generalizable knowledge from diverse training graphs to create a customized model that can be fine-tuned to discover SHSs in new unseen diverse graphs. Our experimental results show that the proposed models are highly effective and efficient. (4) To identify bottleneck structural hole spanner nodes in dynamic networks, we propose a decremental algorithm and graph neural network model. The key idea of our proposed algorithm is to reduce the re-computations by identifying affected nodes due to updates in the network and performing re-computations for affected nodes only. Our graph neural network model considers the dynamic network as a series of snapshots and learns to discover SHS nodes in these snapshots. Our experiments demonstrate that the proposed approaches achieve significant speedup over re-computations for dynamic graphs.Thesis (Ph.D.) -- University of Adelaide, School of Computer and Mathematical Sciences, 202

LIPIcs, Volume 244, ESA 2022, Complete Volume

LIPIcs, Volume 244, ESA 2022, Complete Volum

LIPIcs, Volume 274, ESA 2023, Complete Volume

LIPIcs, Volume 274, ESA 2023, Complete Volum

Responding to Environmental Issues through Adaptive Collaborative Management

Focused on forest management and governance, this book examines two decades of experience with Adaptive Collaborative Management (ACM), assessing both its uses and improvements needed to address global environmental issues. The volume argues that the activation and the empowerment of local peoples are critical to addressing current environmental challenges and that this must be enhanced by linking and extending such stewardship to global and national policymakers and actors on a broader scale. This can be achieved by employing ACM’s participatory approach, characterized by conscious efforts among stakeholders to communicate, collaborate, negotiate and seek out opportunities to learn collectively about the impacts of their action. The case studies presented here reflect decades of experience working with forest communities in three Indonesian Islands and four African countries. Researchers and practitioners who participated in CIFOR’s early ACM work had the rare opportunity to return to their research sites decades later to see what has happened. These authors reflect critically on their own experience and local site conditions to glean insights that guide us in more effectively addressing climate change and other forest-related challenges. They showcase how global and regional actors will have to work more closely with smallholders, Indigenous Peoples and local communities, recognizing the key local roles in forest stewardship. This book will be of great interest to students, scholars and practitioners working in the fields of conservation, forest management, community development, natural resource management and development studies more broadly

Social enterprise-led local development of the circular economy : socio-spatial networks and value-impact scaling pathways

The circular economy (CE) paradigm has emerged to challenge a predominantly linear economic development model by extracting and retaining the highest possible value from existing resources through their recirculation. While CE-related literature and policy discourse continue to grow, there is limited research on socio-spatial mechanisms shaping alternative circular economic development trajectories in the local development context. This thesis considers how the ecological and extra economic premises of CE thinking can be harnessed through mission-driven social enterprises (SEs) aimed at locally tackling poverty, inequality and/or waste. It investigates the extent to which 50 case study SEs operating in three different socio-spatial and institutional contexts (Hull, UK; Santiago, Chile; and Graz, Austria), and across diverse sectors (food, wood, textiles, housing, among others), stimulate and potentially could stimulate the development of a local and socially inclusive CE. In so doing, firstly, it untangles complex socio-material circuits of value and corresponding feedback loops associated with flows of (in)tangible resources across co-existing mainstream and alternative economic spaces of exchange, production and consumption. Secondly, this research adopts a Social Network Analysis approach to map and examine the broader social circular enterprise ecosystem in the City of Hull. It explores how the broader network constellations not only embody, but also could embody symbiotic relationships between environmentally-/CE-, socially- and/or commercially oriented enterprises to foster inclusive CE development. It then offers a heuristic framework illustrating the interplay of factors shaping collaborative ties in the development of inclusive CE. Finally, it explores diverse social-circular impact scaling strategies and develops an Integrated Social-Circular Value-Impact Scaling(ISCIRVIS) framework for academia and context-adaptable toolkit for entrepreneurs. The toolkit is designed to help entrepreneurs to create, deepen and/or broaden the scale and scope of environmental-circular, social and/or economic value outcomes/impacts associated with existing or implementable (circular) activities, yet in the light of potential costs/risks

The Pinkaiti Partnership: A Case Study of Transnational Research and Education in the Brazilian Amazon

In 1991, Barbara Zimmerman visited the Mẽbêngôkre-Kayapó community of A’Ukre. A’Ukre and Zimmerman came up with an idea to create the Pinkaiti Ecological Research Station (Pinkaiti) within the federally demarcated Kayapó Indigenous Territories in Brazil’s Pará state. Pinkaiti was conceptualized to: (1) preserve Kayapó forests; (2) strengthen Kayapó culture; (3) create an economic alternative to regional mahogany logging; (4) initiate a tropical ecology research program; and (5) strengthen Kayapó transnational networks. After leaving A’Ukre, Zimmerman recruited Conservation International, an international environmental nongovernmental organization (NGO) as an institutional partner. The “Pinkaiti Partnership” has since evolved into a research and education-based multi-stakeholder partnership that includes a transnational network of community, NGO, university, and government actors. Over time, the partnership moved through four eras of activity: initiation (1991-1995); early research (1995-2000); international research (2000-2004); and the field course (2004 – present). Using an embedded comparative case study methodology, this dissertation unpacks the trajectory of stakeholder groups (A’Ukre community, NGOs, universities) as units of analysis to discuss the structure, process, and outcomes of partnership activities across partnership eras. To analyze partnership dynamics, I use Pinkaiti as a boundary object to trace Pinkaiti partner interactions across horizontal, vertical, and transversal axes. As a boundary object, Pinkaiti takes on multiple meanings and forms, depending on its use and context, as it is activated simultaneously or independently by one or more partnership actors. Partnership actors engage one another by navigating cultural, geographic, institution, or knowledge passage points. By tracing each actor group’s trajectory through the lens of Pinkaiti, the study illustrates how boundary objects both permit and restrict transnational collaboration. At the same time, the study reveals both the opportunities and limits of boundary objects as a conceptual tool. Boundary objects can be useful for tracking histories, clarifying the big picture, highlighting feedback loops, and illuminating invisible work. On the other hand, the Pinkaiti study shows that boundary objects can be limited in scope, reflect designer biases, and reinforce unequal power dynamics. Still, the Pinkaiti Partnership suggests important takeaways for actors interested in the design, implementation, or evaluation of education or research-based transnational partnership work

Long-term urban resilience – a policy framework

Urbanisation, technical development and climate change have brought the resilience of cities into the sphere of public debate. This and the continued trend of seeing cities as drivers of a country’s fate in the face of uncertainty has in turn made understanding factors that influence outcomes of urban policy even more relevant for academic research and policy-making. However, urban resilience policy is a complex and quickly evolving field characterised by significant challenges associated with urban governance systems, political pressures, uncertain and emergent nature of threats, speed of change and the level of complexity of long-lived networks that form cities. Additionally, resilience literature often lacks a focus on practical implications for urban resilience policy planning, development and implementation that can help policy-makers prioritise action and inform their decisions. This thesis presents a multi-method qualitative research design and aims to help develop a better understanding of practical approaches to long-term resilience-building policy development and implementation at the metropolitan scale across city networks that share a common understanding of urban resilience-building and strive towards it. The research provides a practical framework that can be used to analyse urban resilience policy development and implementation across city networks. The Long-term Urban Resilience Policy Action (LURPA) framework allows to study the state of multiple dimensions and areas of interventions that may influence the long-term implementation of urban resilience policies for metropolitan areas at different levels of governance. Other tools included in the framework allow to visualise complex interrelations that illustrate how policies are implemented. This type of research is needed to help policy-makers identify gaps and opportunities to improve the urban resilience policy over the long-term and to facilitate comprehensive and systematic research for policy learning. The framework was applied to two case studies (Melbourne and Glasgow) which provided specific insight into how some contextual elements that form intrinsic and extrinsic context of cities can hinder or enable the long-term implementation of urban resilience policy in metropolitan areas. Finally, this research also offers a discussion of potential implications of different governance, political, financial, information and reflexivity models for the Australian context

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse