We will review some of the major results in random graphs and some of the more challenging open problems. We will cover algorithmic and structural questions. We will touch on newer models, including those related to the WWW.

Random graphs

The measurement of meaning

行銷硏究 : Marketing research

We explore the effect of dimensionality on the nearest neighbor problem. We show that under a broad set of conditions (much broader than independent and identically distributed dimensions), as dimensionality increases, the distance to the nearest data point approaches the distance to the farthest data point. To provide a practical perspective, we present empirical results on both real and synthetic data sets that demonstrate that this effect can occur for as few as 10-15 dimensions. These results should not be interpreted to mean that high-dimensional indexing is never meaningful; we illustrate this point by identifying some high-dimensional workloads for which this effect does not occur. However, our results do emphasize that the methodology used almost universally in the database literature to evaluate high-dimensional indexing techniques is flawed, and should be modified. In particular, most such techniques proposed in the literature are not evaluated versus simple linear scan, and are evaluated over workloads for which nearest neighbor is not meaningful. Often, even the reported experiments, when analyzed carefully, show that linear scan would outperform the techniques being proposed on the workloads studied in high (10-15) dimensionality!.

When is nearest neighbor meaningful

The State of Food and Agriculture

Improving robustness of complex networks is a challenge in several application domains, such as power grids and water management networks. In such networks, high robustness can be achieved by optimizing graph metrics such as the effective graph resistance, which is the focus of this paper. An important challenge lies in improving the robustness of complex networks under dynamic topological network changes, such as link addition and removal. This paper contributes theoretical and experimental findings about the robustness of complex networks under two scenarios: (i) selecting a link whose addition maximally decreases the effective graph resistance; (ii) protecting a link whose removal maximally increases the effective graph resistance. Upper and lower bounds of the effective graph resistance under these topological changes are derived. Four strategies that select single links for addition or removal, based on topological and spectral metrics, are evaluated on various synthetic and real-world networks. Furthermore, this paper illustrates a novel comparison method by considering the distance between the added or removed links, optimized according to the effective graph resistance and the algebraic connectivity. The optimal links are different in most cases but in close proximity.

Improving robustness of complex networks via the effective graph resistance

Large-scale decentralized systems organized in overlay networks are complex to manage. Such systems integrate organizational complexity in the application-level resulting in low abstraction and modularity in their services. This thesis introduces a multi-level conceptual architecture for overlay services. An overlay service is a large-scale decentralized system organized in overlay networks that provides generic application capabilities. Two overlay services introduced in this thesis provide a proof-of-concept for the higher abstraction and modularity that can be achieved with this architecture. The first overlay service builds and maintains overlay networks that are self-organized in different tree topologies according to different application criteria. The second overlay service performs a function-indepencent and routing-independent decentralized aggregation of values that are distributed in a network and continuously changing. Experimental evaluation studies the performance trade-offs imposed by this generic design. This thesis studies overlay services in the application domain of the Smart Power Grid. More specifically, overlay services are used in demand-side energy management to perform (i) load-shifting and (ii) demand-adjustment in a fully decentralized fashion. The evaluation of this approach is grounded in the current reality and practice of the Smart Power Grid.

Multi-level Reconfigurable Self-organization in Overlay Services

Open sharing of data that are collected with smart devices would empower citizens and create jobs, say Dirk Helbing and Evangelos Pournaras.

/pdf/society-build-digital-democracy-jhxbeis132.pdf

Society: Build digital democracy.

The Internet of Things equips citizens with a phenomenal new means for online participation in sharing economies. When agents self-determine options from which they choose, for instance, their resource consumption and production, while these choices have a collective systemwide impact, optimal decision-making turns into a combinatorial optimization problem known as NP-hard. In such challenging computational problems, centrally managed (deep) learning systems often require personal data with implications on privacy and citizens’ autonomy. This article envisions an alternative unsupervised and decentralized collective learning approach that preserves privacy, autonomy, and participation of multi-agent systems self-organized into a hierarchical tree structure. Remote interactions orchestrate a highly efficient process for decentralized collective learning. This disruptive concept is realized by I-EPOS, the Iterative Economic Planning and Optimized Selections, accompanied by a paradigmatic software artifact. Strikingly, I-EPOS outperforms related algorithms that involve non-local brute-force operations or exchange full information. This article contributes new experimental findings about the influence of network topology and planning on learning efficiency as well as findings on techno-socio-economic tradeoffs and global optimality. Experimental evaluation with real-world data from energy and bike sharing pilots demonstrates the grand potential of collective learning to design ethically and socially responsible participatory sharing economies.

https://dl.acm.org/doi/pdf/10.1145/3277668

Decentralized Collective Learning for Self-managed Sharing Economies

The robustness of smart grids is challenged by unpredictable power peaks or temporal demand oscillations that can cause blackouts and increase supply costs. Planning of demand can mitigate these effects and increase robustness. However, the impact on consumers in regards to the discomfort they experience as a result of improving robustness is usually neglected. This paper introduces a decentralized agent-based approach that quantifies and manages the tradeoff between robustness and discomfort under demand planning. Eight selection functions of plans are experimentally evaluated using real data from two operational smart grids. These functions can provide different quality of service levels for demand-side energy self-management that capture both robustness and discomfort criteria.

/pdf/decentralized-planning-of-energy-demand-for-the-management-2ggdzw0brn.pdf

Evangelos Pournaras

Papers

Improving robustness of complex networks via the effective graph resistance

Multi-level Reconfigurable Self-organization in Overlay Services

Society: Build digital democracy.

Decentralized Collective Learning for Self-managed Sharing Economies

Decentralized Planning of Energy Demand for the Management of Robustness and Discomfort