Graph Relation Aware Continual Learning

Continual graph learning (CGL) studies the problem of learning from an infinite stream of graph data, consolidating historical knowledge, and generalizing it to the future task. At once, only current graph data are available. Although some recent attempts have been made to handle this task, we still face two potential challenges: 1) most of existing works only manipulate on the intermediate graph embedding and ignore intrinsic properties of graphs. It is non-trivial to differentiate the transferred information across graphs. 2) recent attempts take a parameter-sharing policy to transfer knowledge across time steps or progressively expand new architecture given shifted graph distribution. Learning a single model could loss discriminative information for each graph task while the model expansion scheme suffers from high model complexity. In this paper, we point out that latent relations behind graph edges can be attributed as an invariant factor for the evolving graphs and the statistical information of latent relations evolves. Motivated by this, we design a relation-aware adaptive model, dubbed as RAM-CG, that consists of a relation-discovery modular to explore latent relations behind edges and a task-awareness masking classifier to accounts for the shifted. Extensive experiments show that RAM-CG provides significant 2.2%, 6.9% and 6.6% accuracy improvements over the state-of-the-art results on CitationNet, OGBN-arxiv and TWITCH dataset, respective

Clustered marginalization of minorities during social transitions induced by co-evolution of behaviour and network structure

Large-scale transitions in societies are associated with both individual behavioural change and restructuring of the social network. These two factors have often been considered independently, yet recent advances in social network research challenge this view. Here we show that common features of societal marginalization and clustering emerge naturally during transitions in a co-evolutionary adaptive network model. This is achieved by explicitly considering the interplay between individual interaction and a dynamic network structure in behavioural selection. We exemplify this mechanism by simulating how smoking behaviour and the network structure get reconfigured by changing social norms. Our results are consistent with empirical findings: The prevalence of smoking was reduced, remaining smokers were preferentially connected among each other and formed increasingly marginalised clusters. We propose that self-amplifying feedbacks between individual behaviour and dynamic restructuring of the network are main drivers of the transition. This generative mechanism for co-evolution of individual behaviour and social network structure may apply to a wide range of examples beyond smoking.Comment: 16 pages, 5 figure

Continual Learning on Dynamic Graphs via Parameter Isolation

Many real-world graph learning tasks require handling dynamic graphs where new nodes and edges emerge. Dynamic graph learning methods commonly suffer from the catastrophic forgetting problem, where knowledge learned for previous graphs is overwritten by updates for new graphs. To alleviate the problem, continual graph learning methods are proposed. However, existing continual graph learning methods aim to learn new patterns and maintain old ones with the same set of parameters of fixed size, and thus face a fundamental tradeoff between both goals. In this paper, we propose Parameter Isolation GNN (PI-GNN) for continual learning on dynamic graphs that circumvents the tradeoff via parameter isolation and expansion. Our motivation lies in that different parameters contribute to learning different graph patterns. Based on the idea, we expand model parameters to continually learn emerging graph patterns. Meanwhile, to effectively preserve knowledge for unaffected patterns, we find parameters that correspond to them via optimization and freeze them to prevent them from being rewritten. Experiments on eight real-world datasets corroborate the effectiveness of PI-GNN compared to state-of-the-art baselines

Addressing consumerisation of IT risks with nudging

In this work we address the main issues of Information Technology (IT) consumerisation that are related to security risks, and vulnerabilities of devices used within Bring Your Own Device (BYOD) strategy in particular. We propose a ‘soft’ mitigation strategy for user actions based on nudging, widely applied to health and social behaviour influence. In particular, we propose a complementary, less strict, more flexible Information Security policies, based on risk assessment of device vulnerabilities and threats to corporate data and devices, combined with a strategy of influencing security behaviour by nudging. We argue that nudging, by taking into account the context of the decision-making environment, and the fact that the employee may be in better position to make a more appropriate decision, may be more suitable than strict policies in situations of uncertainty of security-related decisions. Several examples of nudging are considered for different tested and potential scenarios in security context

Addressing consumerisation of IT risks with nudging

In this work we address the main issues of Information Technology (IT) consumerisation that are related to security risks, and vulnerabilities of devices used within Bring Your Own Device (BYOD) strategy in particular. We propose a ‘soft’ mitigation strategy for user actions based on nudging, widely applied to health and social behaviour influence. In particular, we propose a complementary, less strict, more flexible Information Security policies, based on risk assessment of device vulnerabilities and threats to corporate data and devices, combined with a strategy of influencing security behaviour by nudging. We argue that nudging, by taking into account the context of the decision-making environment, and the fact that the employee may be in better position to make a more appropriate decision, may be more suitable than strict policies in situations of uncertainty of security-related decisions. Several examples of nudging are considered for different tested and potential scenarios in security context