Influence maximization (IM) selects a small set of seed users to maximize expected diffusion in a social network, typically under the Independent Cascade model. Optimizing only global spread can amplify pre-existing structural inequities: some groups (e.g., demographics, communities, or departments) may receive far less exposure than others even when the overall spread is high. We present FIMMOGA++, a fairness-aware influence maximization framework that formulates IM as a multi-objective optimization problem over expected spread and multiple groupfairness objectives. FIMMOGA++ integrates efficient influence estimation via Reverse Influence Sampling (RIS) with a manyobjective genetic algorithm. Our framework returns a Pareto front of seed sets, explicitly exposing the trade-off between diffusion efficiency and fairness. We show that FIMMOGA++ improves fairness metrics (e.g., Max–Min group coverage and inequality) while remaining competitive in terms of spread and runtime relative to standard IM baselines.
Akash Janardhan Srinivas, Petros Potikas, William B. Andreopoulos et al.· International Conference on...· 0 citations
We study a specific sparse post-processing pipeline for Random Indexing (RI) on kinship analogies in a small fairytales corpus. The published artifacts use uniform RI context accumulation with 200 dimensions and eight nonzeros, followed by one residual graph average, $\mathbf{E}=(1-\alpha)\mathbf{E}_0+\alpha\mathbf{P}\mathbf{E}_0$, where $\mathbf{P}$ is a row-normalized PPMI graph and $\alpha=0.3$. Terminal row normalization and per-dimension median/IQR scaling are then applied. On the Google analogy benchmark's family section, 272 of 506 questions are valid for every seed. Across five paired seeds, the complete pipeline raises accuracy from 19.41\% to 30.74\%, a gain of 11.32 percentage points with a nested-bootstrap 95\% confidence interval of [6.93, 15.89]. Robust scaling alone contributes 3.24 points [1.25, 5.38], while graph averaging without robust scaling contributes 6.18 points [2.63, 9.92]. A separate 40-question general grid does not support a general improvement: the full pipeline changes accuracy by -6.00 points [-13.50, -0.50], and averaging without robust scaling changes it by -6.50 points [-14.50, -0.50]. The supported positive claim is therefore limited to the covered fairytales kinship analogy set; the results do not establish a generally effective embedding method.
Sriram Loganathan, Gokul Anand, Aung Bo Bo et al.· 0 citations
The idea of context is no longer considered secondary in the construction of language-model systems. With the use of local Retrieval-Augmented Generation, even a tiny modification of the prompt or the context might produce another set of retrievals, citations, and ultimately different answers; however, in practice, tests are often performed with only one version of the question. In this work, we suggest a local context-engineering framework for exploring perturbation robustness, reproducibility, and budgetfriendly assessment in one unified pipeline. Local RAG is built on several small teaching packs for the models; perturbations are introduced to the queries, each experiment is recorded in capsule format, and a gate mechanism based on the decision tree is used to judge if the whole perturbation suite can be skipped, minimized, or run. As a result, the augmented capsule-derived data set contains 3,570 perturbation rows, out of which 2,619 labeled rows are used for retraining. In this labeled subset, BLEU scores are available in 446 cases, answer perplexity and semanticjudgment scores are available in 1,668 cases, citation overlap is available for all rows, and retrieval overlap is available for 2,099 rows. The final decision tree obtains 0.915 ROC-AUC, 0.891 PR-AUC, 0.913 accuracy, 0.863 F1 score, and expected savings of 76.5% on the held-out augmented test split. A more conservative threshold setting lowers the stable-risk value from 0.084 to 0.062, while reducing expected savings to 57.4%. Thus, we provide a local study pipeline to examine the effect of perturbations on answer stability, contradictions, robustness, and budget-friendly evaluation.
Rahul Reddy Gangapuram, William B. Andreopoulos· International Conference on...· 0 citations