Graph Neural Networks in Algorithm Engineering

Graf Nevrale Nettverk (GNN) er ny teknologi innen kunstig intelligens. GNN viderefører de vellykkede ideene fra konvensjonell dyplæring til å fungere på grafer. For ulike grafteoretiske problemer har GNN blitt et nytt verktøy som både kan åpne for nye muligeheter og heve eksisterende metoder. Denne avhandlingen fokuserer på bruk av GNN-er for å løse grafproblemer i praksis. Selv om denne nye teknologien kan være nyttig, er det også klart at på nåværende tidspunkt kan ikke GNN-er konkurrere direkte med tradisjonelle grafalgoritmer. Istedet fokuserer vi på å bruke GNN-er som en komponent sammen med konvensjonelle algoritmeteknikker for å utvikle nye algoritmer. Resultatene som blir presentert i denne avhandlingen viser at dette kan føre til bedre og raskere algoritmer for å løse ulike NP-harde grafproblemer i praksis. For Minste Vektede Nodedekkeproblemet presenterer vi en ny heuristikk som kombinerer reduksjonsregler, GNN-er og lokalt søk. Mens reduksjonsreglene og lokalt søk er de viktigste komponentene, viser vi at GNN-en forbedrer resultatene våre ytterligere. For komplementproblemet, Største Vektede Uavhengige Sett, introduserer vi også en ny metode for å anvende reduksjonsregler raskere ved å bruke GNN-er. Her brukes flere GNN-er for å bestemme hvilke noder som kan reduseres så at vi kan unngå å tape tid på å prøve reduksjonsregler hvor det er usannsynlig at grafen kan reduseres. Etter å ha redusert en graf, brukes ofte en type algoritme basert på forgrening for å finne en optimal løsning. For slike algoritmer viser vi at GNN-er kan brukes for å bestemme hvilken node algoritmen bør forgrene på i neste steg. En fordel med GNN-er er at beregningene som trengs kan akselereres på flere måter. Dette inkluderer spesialiserte instruksjoner og parallellprosessering. Med dette viser vi at små GNN-modeller kan oppnå samme kjøretider som enkle grådige algoritmer samtidig som de kan ha bedre parallell skalerbarhet. På denne måten klarer vi å utkonkurrere konvensjonelle parallelle grådige algoritmer for Graf-Fargeleggingsproblemet. Selv om GNN-er kan være nyttige verktøy, er de sjeldent den sentrale komponenten i en algoritme, i hvert fall ikke i de beste programmene for denne typen problemer. Dette er tydelig ved å se hvilke teknikker som blir brukt i den årlige programmeringskonkurransen PACE (Parameterized Algorithms and Computational Experiments Challenge). I PACE har hvert lag seks måneder på utvikle algoritmer for å løse et gitt NP-hardt grafproblem. Det har likevel ikke vært brukt GNN-er av noen lag siden konkurransen starten i 2015. Som det siste kapittelet i denne avhandlingen presenterer vi vårt vinnende bidrag til 2024-iterasjonen av PACE. Til tross for at dette resultatet ikke inkluderer maskinlæring har PACE vært en viktig del gjennom hele PhD-prosjektet.Graph Neural Networks (GNNs) are recent additions to the field of artificial intelligence. They adapt the successful ideas from conventional deep learning to the irregular structure found in graphs. For various graph theoretical problems, GNNs have become a new tool that can enable new solutions and elevate existing ones. This thesis focuses on using GNNs to solve graph problems in practice. While this new technology can be powerful, it is clear that the current state of GNNs cannot stand up to traditional graph algorithms on their own. Instead, we focus on using GNNs as a component along with conventional algorithmc techniques to develop new algorithms. The results presented in this thesis demonstrate that this can lead to better and faster algorithms for solving various NP-hard graph problems in practice. For the Minimum Weight Vertex Cover problem, we present a new heuristic that combines data reduction rules, GNNs, and local search. While the reduction rules and local search are the most important components, we show that the GNNs further elevate our results. For the complement problem, Maximum Weight Independent Set, we also introduce a new way of accelerating the application of data reduction rules using GNNs. Here, the GNNs are used for early screening to avoid losing time trying to reduce parts of the graph that are unlikely to be reduced. After reducing an input instance, a branch-and-bound algorithm is often used to find an optimal solution. For such algorithms, we show that GNNs can guide the algorithm and decide where to branch next. A benefit of GNNs is that the computations involved when using them can be accelerated in several ways. This acceleration includes specialized instructions and parallel processing. With this, we show that small GNN models can reach the same running times as simple greedy algorithms while having better parallel scalability. This allowed us to outperform conventional parallel greedy algorithms for the Graph Coloring problem. While GNNs can be powerful tools, they are rarely the primary component of an algorithm, at least not in state-of-the-art programs. This is clear when considering the annual programming competition PACE (Parameterized Algorithms and Computational Experiments Challenge). In PACE, teams have six months to work on algorithms to solve a given NP-hard graph problem. Still, GNNs have not appeared in any high-ranking submission since its inception in 2015. As the last chapter in this thesis, we present our winning submission to the 2024 iteration of the PACE challenge. Despite the lack of machine learning in this result, the PACE challenge played an important part throughout the PhD project.Doktorgradsavhandlin

Investigation of resistivity, porosity and pore solution composition in carbonated mortar prepared with ordinary Portland cement and Portland-fly ash cement

This project investigated how carbonation affected resistivity in mortars made with different cement types. The goal was to explain this by investigating the pore solution composition, the pore structure and the moisture content in mortars made with different cements and exposed to different relative humidity and CO2-concentrations. Mortar samples made with CEM I and CEM II/B-V were cured for 14 days and exposed to CO2 for 27 weeks before testing. The resistivity was measured using embedded titanium bars in the mortar samples. The pore structure was investigated using the PF-method. The extent of carbonation was measured using thermogravimetric analysis. The pore solution composition was investigated with cold water extraction and pore solution expression followed by analysis by ICP-MS. The impact of carbonation in the different RH-conditions could not be concluded, as the mortars stored at 90% RH and 5% CO2 had not fully carbonated within the course of the project. Carbonation caused the resistivity to increase drastically for both cement types. The resistivity of mortars made with CEM II/B-V were found to be higher than that of mortars made with CEM I, both in carbonated and non-carbonated state, but the ratio between the two cements could indicate that carbonation may have a bigger impact on the resistivity than the type of cement has. Carbonation decreased the moisture content and pore volume in the mortars from both cement types. The mortars made with CEM II/B-V showed a larger pore volume than mortars made with CEM I in all exposure conditions. The degree of capillary saturation was found to be related to resistivity, as a lower degree of saturation corresponded to a higher resistivity. The pore solution composition also changed upon carbonation. A significant drop in the concentration of Na and K was seen upon carbonation. In the non-carbonated samples, the samples from CEM I showed a higher content of Na and K compared to samples from CEM II/B-V, whereas the Na and K content was similar for both cement types after carbonation. Both moisture content, degree of capillary saturation and pore solution composition appears to influence the resistivity, but it was not possible to conclude to which extent each parameter influenced the resistivity

The why, what, and how of Brand Boycott

Brand boycotts are becoming more and more common. There is an increased focus on ethical brand behavior. Consumers boycott brands that are not living up to their expectations. The advancement of social media makes boycotts easy to organize. While the number of brand boycotts has been increasing, the literature on the subject is still quite limited. This study is looking into the reasons behind brand boycott, the outcomes of brand boycott, and the strategies brands can use to respond to brand boycotts. Prior literature has focused on factors affecting consumer participation in boycott. The literature review gives an overview of previous topics covered within brand boycott. Mostly, the studies have been correlational and examined brand boycott in particular environments. With recent developments in the field there is a need for conceptual study. Secondary data was used in this study. The authors collected data from YouTube videos, news articles, and blogs or other websites. The data was sorted in an Excel document and sorted into open codes by both the authors. From the open codes, aggregate dimensions and subcategories were generated. The authors found the aggregate dimensions of reasons behind brand boycott, outcomes of brand boycott and strategies for brands to respond. The results showed that brands can respond to brand boycotts using passive or active strategies, there are negative and positive outcomes for both the organization and the consumer, and there are four main reasons why brand boycotts occur. The disidentification theory and complexity theory are used to help further contextualize the findings. Contributions of this study are filling the gaps in the literature, and providing a framework which brands can use to navigate between brand boycotts today. Consumers and brands must prepare for an increasing amount of brand boycotts in the future. The findings of this study present a framework for both consumers and brands on how to navigate the increasing amount of brand boycotts

Concurrent Iterated Local Search for the Maximum Weight Independent Set Problem

The Maximum Weight Independent Set problem is a fundamental NP-hard problem in combinatorial optimization with several real-world applications. Given an undirected vertex-weighted graph, the problem is to find a subset of the vertices with the highest possible weight under the constraint that no two vertices in the set can share an edge. This work presents a new iterated local search heuristic called CHILS (Concurrent Hybrid Iterated Local Search). The implementation of CHILS is specifically designed to handle large graphs of varying densities. CHILS outperforms the current state-of-the-art on commonly used benchmark instances, especially on the largest instances. As an added benefit, CHILS can run in parallel to leverage the power of multicore processors. The general technique used in CHILS is a new concurrent metaheuristic called Concurrent Difference-Core Heuristic that can also be applied to other combinatorial problems

Targeted Branching for the Maximum Independent Set Problem Using Graph Neural Networks

Identifying a maximum independent set is a fundamental NP-hard problem. This problem has several real-world applications and requires finding the largest possible set of vertices not adjacent to each other in an undirected graph. Over the past few years, branch-and-bound and branch-and-reduce algorithms have emerged as some of the most effective methods for solving the problem exactly. Specifically, the branch-and-reduce approach, which combines branch-and-bound principles with reduction rules, has proven particularly successful in tackling previously unmanageable real-world instances. This progress was largely made possible by the development of more effective reduction rules. Nevertheless, other key components that can impact the efficiency of these algorithms have not received the same level of interest. Among these is the branching strategy, which determines which vertex to branch on next. Until recently, the most widely used strategy was to choose the vertex of the highest degree. In this work, we present a graph neural network approach for selecting the next branching vertex. The intricate nature of current branch-and-bound solvers makes supervised and reinforcement learning difficult. Therefore, we use a population-based genetic algorithm to evolve the model’s parameters instead. Our proposed approach results in a speedup on 73% of the benchmark instances with a median speedup of 24%

Peer Selection in Peer-to-Peer Streaming Systems

One important task of any peer-to-peer streaming system (p2p-ss) is how to choose which peers should connect to which peers. How well a p2p-ss perform this task greatly influences its performance. This thesis explores how different peer selection algorithms affect the performance of such systems. A framework for doing the comparisons of peer selection algorithms is built on top of the network simulator ns2, making it possible to later extend the simulations with new peer selection algorithms, congestion control algorithms, wireless networks, cross traffic and other. However, ns2 is a low-level simulator, hence limiting the number of peers in the simulations, because CPU-resources are limited. The simulations are limited to single-layered streams. We find that a centralized selection method, which utilizes knowledge of bandwidth capacities and routing in the network, greatly outperforms both simple random selection of peers, and selection of close peers. Even though centralized selection does not scale well, and is therefore only applicable for a limited number of peers, this shows there is much room for improvement over basic strategies

Targeted Branching for the Maximum Independent Set Problem Using Graph Neural Networks

Identifying a maximum independent set is a fundamental NP-hard problem. This problem has several real-world applications and requires finding the largest possible set of vertices not adjacent to each other in an undirected graph. Over the past few years, branch-and-bound and branch-and-reduce algorithms have emerged as some of the most effective methods for solving the problem exactly. Specifically, the branch-and-reduce approach, which combines branch-and-bound principles with reduction rules, has proven particularly successful in tackling previously unmanageable real-world instances. This progress was largely made possible by the development of more effective reduction rules. Nevertheless, other key components that can impact the efficiency of these algorithms have not received the same level of interest. Among these is the branching strategy, which determines which vertex to branch on next. Until recently, the most widely used strategy was to choose the vertex of the highest degree. In this work, we present a graph neural network approach for selecting the next branching vertex. The intricate nature of current branch-and-bound solvers makes supervised and reinforcement learning difficult. Therefore, we use a population-based genetic algorithm to evolve the model’s parameters instead. Our proposed approach results in a speedup on 73% of the benchmark instances with a median speedup of 24%

Pårørendes roller i rehabilitering : en kvalitativ studie

Bakgrunnen for masteroppgaven er kliniske og forskningsbaserte erfaringer som tyder på at pårørende til brukere av rehabiliteringstjenester har en uklar rolle og blir lite involvert i rehabilitering. Gjennom juridiske og politiske føringer vektlegges betydningen av samarbeid med pårørende i helsetjenester, men det synes å være et gap mellom teori og praksis. Hensikten med studien er å finne ut mer om hvordan pårørende til eldre rehabiliteringsbrukere opplever og utøver rollen som pårørende gjennom samhandling med eget familiemedlem og rehabiliteringspersonell i en kommunal rehabiliteringskontekst. Studiens problemstilling er: ”Hvordan opplever og utøver pårørende til eldre brukere sin rolle i rehabiliteringsprosessen?” Teoretisk forankring: Masteroppgaven er inspirert av symbolsk interaksjonisme og Goffmans teori om hvordan roller blir formet gjennom hverdagslig samhandling. I tillegg belyses intervjupersonenes arbeid med å gjenopprette kontinuitet i hverdagspraksis ved hjelp av teorier om mestring (Bury og Antonovsky). Metode: Studien har en fortolkende design med innsamling av kvalitative data ved hjelp av halvstrukturerte intervjuer. Resultater: Studien viser hvordan seks kvinner på ulike måter har fått endret en allerede etablert hverdag som pårørende, og har fått nye oppgaver etter at deres familiemedlem ble syk eller skadet. De spiller ut den nye rollen på ulike måter i forskjellige situasjoner, avhengig av hvilke begrensninger og muligheter de har. Gjennom fortellinger om samhandling med eget familiemedlem og rehabiliteringspersonell kommer det til syne forhandlinger om en ny sosial orden. Det synliggjøres også en ubalanse i makt mellom pårørende og rehabiliteringspersonell. Det kreves pårørendekompetanse og utvikling av mestringsstrategier for å lykkes i å etablere et samarbeid med rehabiliteringspersonell, men også for å sette tydelige grenser for seg selv på en måte som rehabiliteringspersonalet og eget familiemedlem aksepterer og finner passende. Rehabiliteringspersonell bør være bevisst på de sammenhenger som pårørende utformer og utøver sin rolle i, og utvise ydmykhet i møte med dem og deres mestringsstrategier.Background for this master thesis are clinical experiences and evidence indicating that relatives to users of rehabilitation services have an unclear role, and that they are not being involved in rehabilitation. Legal and political guidance emphasize the importance of collaboration with relatives in health care, but there seem to be a gap between theory and practice. The purpose of this study is to explore how relatives to elderly people who have undergone community-rehabilitation manage the role as informal carers, through interaction with their family members and health care workers within a rehabilitation context in the community. Problem: How do informal carers of elderly people experience and manage their role in the rehabilitation process? Literature review: The master thesis is inspired by symbolic interaction and Erving Goffmans theory of how roles are formed through everyday interactions. How the informal carers try to restore their everyday practice is discussed by theories of coping (Bury and Antonovsky). Method: The study has an interpreting design including collecting of qualitative data using half structured interviews. Results: The study shows how six women in various ways get their established everyday life changed, and get new role responsibilities after their family member becomes ill or injured. They manage their new role in various ways in different situations, depending of the constraints and opportunities they have. Informal carer –professional carer interaction reveals the ways they negotiate a new social order, and that there is unequal power in the relationship between informal carers and healthcare workers. It requires competence and development of coping strategies to succeed in establishing a partnership and setting limits in a way that their family member and the professional carers agree and find acceptable. Healthcare workers ought to be aware of the context in which informal carers design and manage their roles, and show respect in the meeting with them and their coping strategies.Master i rehabiliterin